Updated on 2024/05/21

写真a

 
TAGUCHI Yoshihiro
 
Organization
Faculty of Science and Engineering Professor
Other responsible organization
Physics Course of Graduate School of Science and Engineering, Master's Program
Physics Course of Graduate School of Science and Engineering, Doctoral Program
Contact information
The inquiry by e-mail is 《here
Profile

I am studying bioinformatics, especially focusing on gene expression analysis as well as multi omics data ayalysis. Currently, my primary interest is applying tensor decomposition for selecting variables in the unsupervised manner.

 

I was selected as top 2% reserachers in the field of Bioinformatics by Stanford University at 20232022 and 2021

I have published a monograph on my research from Springer at Sep. 2019 (Click the below image).


Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach

Here is an introduction to the above book.

I also editied a book on my field.

Handbook of Machine Learning Applications for Genomics

External link

Degree

  • 理学博士 ( 東京工業大学 )

Education

  • 1988.3
     

    Tokyo Institute of Technology   Graduate School, Division of Science and Engineering   Department of Physics   doctor course   completed

  • 1986.3
     

    Tokyo Institute of Technology   Graduate School, Division of Science and Engineering   master course   completed

  • 1984.3
     

    Tokyo Institute of Technology   Faculty of Science   Department of Applied Physics   graduated

Research History

  • 2006.4 - Now

    Chuo University   Department of Physics,   Professor

  • 2023.4 - 2023.9

    Hiroshima University   Visiting Professor

  • 2009.3 - 2010.3

    European Bioinformatics Institute   Visiting Professor

  • 1997.4 - 2006.3

    Chuo University   Faculty of Science and Engineering Department of Physics   Associate Professor,

  • 2004.4 - 2005.3

    University of the Ryukyus   Faculty of Science

  • 2001.4 - 2002.3

    Waseda University   Graduate School of Social Sciences

  • 2000.4 - 2001.3

    Tokyo Institute of Technology   Graduate School of Science and Engineering

  • 1999.4 - 2000.3

    Nagoya University   School of Science

  • 1998.4 - 1999.3

    Shizuoka University   Faculty of Science

  • 1997.4 - 1998.3

    Tokyo University of Agriculture and Technology   Faculty of Engineering

  • 1988.4 - 1997.3

    Tokyo Institute of Technology   Department of Physics   Assistant Professor,

  • 1989.10 - 1990.9

    Postdoctoral Fellow, Forschungszentrum Juelich GmbH

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Professional Memberships

  • 日本物理学会

  • 情報処理学会

  • 日本バイオインフォマティクス学会

  • 日本RNA学会

  • 日本消化器病学会

  • 日本癌学会

  • The Physical Society of Japan

  • Information Processing Society of Japan

  • The Japanese Society for Bioinformatics

  • The RNA Society of Japan

  • The Japanese Society of Gastroenterology

  • Japanese Cancer Association

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Research Interests

  • microRNA

  • Nonlinear Physics

  • Bioinformatics

  • 非線形物理学

  • バイオインフォマティクス

  • tensor decomposition

  • Machine Learning

  • In silico drug design

  • feature selection

  • gene expression profile

  • transcriptome

  • epigenome

Research Areas

  • Informatics / Life, health and medical informatics

Papers

  • Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through Deep Transfer Learning Approach

    Turki Turki, Sarah Al Habib, Y-h. Taguchi

    Mathematics   2024.4

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    Publishing type:Research paper (scientific journal)  

    DOI: 10.1101/2024.04.22.590420

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  • Analysis of Methicillin Resistance inStaphylococcus AureusSepsis Using TDbasedUFE

    Shotaro Watanabe, Y-h. Taguchi

    2024.1

  • Characterization of circulating <scp>miRNAs</scp> in the treatment of primary liver tumors Reviewed International journal

    Tomohiro Umezu, Shogo Tanaka, Shoji Kubo, Masaru Enomoto, Akihiro Tamori, Takahiro Ochiya, Y.‐H. Taguchi, Masahiko Kuroda, Yoshiki Murakami

    Cancer Reports   2023.12

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Wiley  

    Abstract

    Background and Aim

    Circulating micro RNAs (miRNAs) indicate clinical pathologies such as inflammation and carcinogenesis. In this study, we aimed to investigate whether miRNA expression level patterns in could be used to diagnose hepatocellular carcinoma (HCC) and biliary tract cancer (BTC), and the relationship miRNA expression patterns and cancer etiology.

    Methods

    Patients with HCC and BTC with indications for surgery were selected for the study. Total RNA was extracted from the extracellular vesicle (EV)‐rich fraction of the serum and analyzed using Toray miRNA microarray. Samples were divided into two cohorts in order of collection, the first 85 HCC were analyzed using a microarray based on miRBase ver.2.0 (hereafter v20 cohort), and the second 177 HCC and 43 BTC were analyzed using a microarray based on miRBase ver.21 (hereafter v21 cohort).

    Results

    Using miRNA expression patterns, we found that HCC and BTC could be identified with an area under curve (AUC) 0.754 (v21 cohort). Patients with anti‐hepatitis C virus (HCV) treatment (SVR‐HCC) and without antiviral treatment (HCV‐HCC) could be distinguished by an AUC 0.811 (v20 cohort) and AUC 0.798 (v21 cohort), respectively.

    Conclusions

    In this study, we could diagnose primary hepatic malignant tumor using miRNA expression patterns. Moreover, the difference of miRNA expression in SVR‐HCC and HCV‐HCC can be important information for enclosing cases that are prone to carcinogenesis after being cured with antiviral agents, but also for uncovering the mechanism for some carcinogenic potential remains even after persistent virus infection has disappeared.

    DOI: 10.1002/cnr2.1964

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  • Investigating Neuron Degeneration in Huntington’s Disease Using RNA-Seq Based Transcriptome Study Reviewed International coauthorship International journal

    Nela Pragathi Sneha, S. Akila Parvathy Dharshini, Y.-h. Taguchi, M. Michael Gromiha

    Genes   14 ( 9 )   1801 - 1801   2023.9

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    Publishing type:Research paper (scientific journal)   Publisher:MDPI AG  

    Huntington’s disease (HD) is a progressive neurodegenerative disorder caused due to a CAG repeat expansion in the huntingtin (HTT) gene. The primary symptoms of HD include motor dysfunction such as chorea, dystonia, and involuntary movements. The primary motor cortex (BA4) is the key brain region responsible for executing motor/movement activities. Investigating patient and control samples from the BA4 region will provide a deeper understanding of the genes responsible for neuron degeneration and help to identify potential markers. Previous studies have focused on overall differential gene expression and associated biological functions. In this study, we illustrate the relationship between variants and differentially expressed genes/transcripts. We identified variants and their associated genes along with the quantification of genes and transcripts. We also predicted the effect of variants on various regulatory activities and found that many variants are regulating gene expression. Variants affecting miRNA and its targets are also highlighted in our study. Co-expression network studies revealed the role of novel genes. Function interaction network analysis unveiled the importance of genes involved in vesicle-mediated transport. From this unified approach, we propose that genes expressed in immune cells are crucial for reducing neuron death in HD.

    DOI: 10.3390/genes14091801

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  • Application note: TDbasedUFE and TDbasedUFEadv: bioconductor packages to perform tensor decomposition based unsupervised feature extraction Reviewed International coauthorship International journal

    Y-h. Taguchi, Turki Turki

    Frontiers in Artificial Intelligence   6   - 1237542   2023.9

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Frontiers Media SA  

    Motivation

    Tensor decomposition (TD)-based unsupervised feature extraction (FE) has proven effective for a wide range of bioinformatics applications ranging from biomarker identification to the identification of disease-causing genes and drug repositioning. However, TD-based unsupervised FE failed to gain widespread acceptance due to the lack of user-friendly tools for non-experts.

    Results

    We developed two bioconductor packages—TDbasedUFE and TDbasedUFEadv—that enable researchers unfamiliar with TD to utilize TD-based unsupervised FE. The packages facilitate the identification of differentially expressed genes and multiomics analysis. TDbasedUFE was found to outperform two state-of-the-art methods, such as DESeq2 and DIABLO.

    Availability and implementation

    TDbasedUFE and TDbasedUFEadv are freely available as R/Bioconductor packages, which can be accessed at https://bioconductor.org/packages/TDbasedUFE and https://bioconductor.org/packages/TDbasedUFEadv, respectively.

    DOI: 10.3389/frai.2023.1237542

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  • GENEvaRX: A novel AI-driven method and web tool can identify critical genes and effective drugs for Lichen Planus Reviewed International coauthorship International journal

    Turki Turki, Y-h. Taguchi

    Engineering Applications of Artificial Intelligence   124   106607 - 106607   2023.9

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Elsevier BV  

    DOI: 10.1016/j.engappai.2023.106607

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  • Integrated Analysis of Gene Expression and Protein–Protein Interaction with Tensor Decomposition Reviewed International coauthorship International journal

    Y-H. Taguchi, Turki Turki

    Mathematics   11 ( 17 )   3655 - 3655   2023.8

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:MDPI AG  

    Integration of gene expression (GE) and protein–protein interaction (PPI) is not straightforward because the former is provided as a matrix, whereas the latter is provided as a network. In many cases, genes processed with GE analysis are refined further based on a PPI network or vice versa. This is hardly regarded as a true integration of GE and PPI. To address this problem, we proposed a tensor decomposition (TD)-based method that can integrate GE and PPI prior to any analyses where PPI is also formatted as a matrix to which singular value decomposition (SVD) is applied. Integrated analyses with TD improved the coincidence between vectors attributed to samples and class labels over 27 cancer types retrieved from The Cancer Genome Atlas Program (TCGA) toward five class labels. Enrichment using genes selected with this strategy was also improved with the integration using TD. The PPI network associated with the information on the strength of the PPI can improve the performance than PPI that stores only if the interaction exists in individual pairs. In addition, even restricting genes to the intersection of GE and PPI can improve coincidence and enrichment.

    DOI: 10.3390/math11173655

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  • Optimized Tensor Decomposition and Principal Component Analysis Outperforming State-of-the-Art Methods When Analyzing Histone Modification Chromatin Immunoprecipitation Profiles Reviewed International coauthorship International journal

    Turki Turki, Sanjiban Sekhar Roy, Y.-H. Taguchi

    Algorithms   16 ( 9 )   401 - 401   2023.8

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    Authorship:Last author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:MDPI AG  

    It is difficult to identify histone modification from datasets that contain high-throughput sequencing data. Although multiple methods have been developed to identify histone modification, most of these methods are not specific to histone modification but are general methods that aim to identify protein binding to the genome. In this study, tensor decomposition (TD) and principal component analysis (PCA)-based unsupervised feature extraction with optimized standard deviation were successfully applied to gene expression and DNA methylation. The proposed method was used to identify histone modification. Histone modification along the genome is binned within the region of length L. Considering principal components (PCs) or singular value vectors (SVVs) that PCA or TD attributes to samples, we can select PCs or SVVs attributed to regions. The selected PCs and SVVs further attribute p-values to regions, and adjusted p-values are used to select regions. The proposed method identified various histone modifications successfully and outperformed various state-of-the-art methods. This method is expected to serve as a de facto standard method to identify histone modification. For reproducibility and to ensure the systematic analysis of our study is applicable to datasets from different gene expression experiments, we have made our tools publicly available for download from gitHub.

    DOI: 10.3390/a16090401

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  • Integrated analysis of human DNA methylation, gene expression, and genomic variation in iMETHYL database using kernel tensor decomposition-based unsupervised feature extraction Reviewed International journal

    Y-h. Taguchi, Shohei Komaki, Yoichi Sutoh, Hideki Ohmomo, Yayoi Otsuka-Yamasaki, Atsushi Shimizu

    PLOS ONE   18 ( 8 )   e0289029 - e0289029   2023.8

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Public Library of Science (PLoS)  

    Integrating gene expression, DNA methylation, and genomic variants simultaneously without location coincidence (i.e., irrespective of distance from each other) or pairwise coincidence (i.e., direct identification of triplets of gene expression, DNA methylation, and genomic variants, and not integration of pairwise coincidences) is difficult. In this study, we integrated gene expression, DNA methylation, and genome variants from the iMETHYL database using the recently proposed kernel tensor decomposition-based unsupervised feature extraction method with limited computational resources (i.e., short CPU time and small memory requirements). Our methods do not require prior knowledge of the subjects because they are fully unsupervised in that unsupervised tensor decomposition is used. The selected genes and genomic variants were significantly targeted by transcription factors that were biologically enriched in KEGG pathway terms as well as in the intra-related regulatory network. The proposed method is promising for integrated analyses of gene expression, methylation, and genomic variants with limited computational resources.

    DOI: 10.1371/journal.pone.0289029

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  • Advanced Tensor Decomposition-Based Integrated Analysis of Protein-Protein Interaction with Cancer Gene Expression Can Improve Coincidence with Clinical Labels Reviewed International coauthorship International journal

    Y-h. Taguchi, Turki Turki

    2023 11th International Conference on Bioinformatics and Computational Biology (ICBCB)   19 - 25   2023.4

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    Authorship:Lead author, Corresponding author   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/icbcb57893.2023.10246633

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  • Signal identification without signal formulation

    Yoh-ichi Mototake, Y-h. Taguchi

    2023.4

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    Publisher:arXiv  

    When there are signals and noises, physicists try to identify signals by modeling them, whereas statisticians oppositely try to model noise to identify signals. In this study, we applied the statisticians' concept of signal detection of physics data with small-size samples and high dimensions without modeling the signals. Most of the data in nature, whether noises or signals, are assumed to be generated by dynamical systems; thus, there is essentially no distinction between these generating processes. We propose that the correlation length of a dynamical system and the number of samples are crucial for the practical definition of noise variables among the signal variables generated by such a system. Since variables with short-term correlations reach normal distributions faster as the number of samples decreases, they are regarded to be ``noise-like'' variables, whereas variables with opposite properties are ``signal-like'' variables. Normality tests are not effective for data of small-size samples with high dimensions. Therefore, we modeled noises on the basis of the property of a noise variable, that is, the uniformity of the histogram of the probability that a variable is a noise. We devised a method of detecting signal variables from the structural change of the histogram according to the decrease in the number of samples. We applied our method to the data generated by globally coupled map, which can produce time series data with different correlation lengths, and also applied to gene expression data, which are typical static data of small-size samples with high dimensions, and we successfully detected signal variables from them. Moreover, we verified the assumption that the gene expression data also potentially have a dynamical system as their generation model, and found that the assumption is compatible with the results of signal extraction.

    DOI: 10.48550/ARXIV.2304.06522

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  • Tensor decomposition discriminates tissues using scATAC-seq Reviewed International coauthorship International journal

    Y.-H. Taguchi, Turki Turki

    Biochimica et Biophysica Acta (BBA) - General Subjects   1867 ( 6 )   130360 - 130360   2023.4

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Elsevier BV  

    ATAC-seq is a powerful tool for measuring the landscape structure of a chromosome. scATAC-seq is a recently updated version of ATAC-seq performed in a single cell. The problem with scATAC-seq is data sparsity and most of the genomic sites are inaccessible. Here, tensor decomposition (TD) was used to fill in missing values. In this study, TD was applied to massive scATAC-seq datasets generated by approximately 200 bp intervals, and this number can reach 13,627,618. Currently, no other methods can deal with large sparse matrices. The proposed method could not only provide UMAP embedding that coincides with tissue specificity, but also select genes associated with various biological enrichment terms and transcription factor targeting. This suggests that TD is a useful tool to process a large sparse matrix generated from scATAC-seq.

    DOI: 10.1016/j.bbagen.2023.130360

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  • Principal component analysis- and tensor decomposition-based unsupervised feature extraction to select more suitable differentially methylated cytosines: Optimization of standard deviation versus state-of-the-art methods Reviewed International coauthorship International journal

    Y.-H. Taguchi, Turki Turki

    Genomics   110577 - 110577   2023.2

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    DOI: 10.1016/j.ygeno.2023.110577

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  • A new machine learning based computational framework identifies therapeutic targets and unveils influential genes in pancreatic islet cells Reviewed International coauthorship International journal

    Turki Turki, Y-h. Taguchi

    Gene   853   147038 - 147038   2023.2

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    Authorship:Last author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Elsevier BV  

    DOI: 10.1016/j.gene.2022.147038

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  • Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome Reviewed International journal

    Suguru Fujita, Yasuaki Karasawa, Ken-ichi Hironaka, Y.-h. Taguchi, Shinya Kuroda

    PLOS ONE   18 ( 2 )   e0281594 - e0281594   2023.2

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Public Library of Science (PLoS)  

    High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually complementary; however, a direct comparison of these two analyses for omics data is poorly examined.We applied tensor decomposition (TD) to a dataset representing changes in the concentrations of 562 blood molecules at 14 time points in 20 healthy human subjects after ingestion of 75 g oral glucose. We characterized each molecule by individual dependence (constant or variable) and time dependence (later peak or early peak). Three of the four features extracted by TD were characterized by our previous hypothesis-driven study, indicating that TD can extract some of the same features obtained by hypothesis-driven analysis in a non-biased manner. In contrast to the years taken for our previous hypothesis-driven analysis, the data-driven analysis in this study took days, indicating that TD can extract biological features in a non-biased manner without the time-consuming process of hypothesis generation.

    DOI: 10.1371/journal.pone.0281594

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  • Bioinformatic tools for epitranscriptomics Invited Reviewed International journal

    Y-h. Taguchi

    American Journal of Physiology-Cell Physiology   324 ( 2 )   C447 - C457   2023.2

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:American Physiological Society  

    The epitranscriptome, defined as RNA modifications that do not involve alterations in the nucleotide sequence, is a popular topic in the genomic sciences. Because we need massive computational techniques to identify epitranscriptomes within individual transcripts, many tools have been developed to infer epitranscriptomic sites as well as to process data sets using high-throughput sequencing. In this review, we have summarized recent developments in epitranscriptome spatial detection and data analysis and discussed their progression.

    DOI: 10.1152/ajpcell.00437.2022

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  • Integrative Meta-Analysis of Huntington’s Disease Transcriptome Landscape Reviewed International coauthorship International journal

    Nela Pragathi Sneha, S. Akila Parvathy Dharshini, Y.-H. Taguchi, M. Michael Gromiha

    Genes   13 ( 12 )   2385 - 2385   2022.12

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    Publishing type:Research paper (scientific journal)   Publisher:MDPI AG  

    Huntington’s disease (HD) is a neurodegenerative disorder with autosomal dominant inheritance caused by glutamine expansion in the Huntingtin gene (HTT). Striatal projection neurons (SPNs) in HD are more vulnerable to cell death. The executive striatal population is directly connected with the Brodmann Area (BA9), which is mainly involved in motor functions. Analyzing the disease samples from BA9 from the SRA database provides insights related to neuron degeneration, which helps to identify a promising therapeutic strategy. Most gene expression studies examine the changes in expression and associated biological functions. In this study, we elucidate the relationship between variants and their effect on gene/downstream transcript expression. We computed gene and transcript abundance and identified variants from RNA-seq data using various pipelines. We predicted the effect of genome-wide association studies (GWAS)/novel variants on regulatory functions. We found that many variants affect the histone acetylation pattern in HD, thereby perturbing the transcription factor networks. Interestingly, some variants affect miRNA binding as well as their downstream gene expression. Tissue-specific network analysis showed that mitochondrial, neuroinflammation, vasculature, and angiogenesis-related genes are disrupted in HD. From this integrative omics analysis, we propose that abnormal neuroinflammation acts as a two-edged sword that indirectly affects the vasculature and associated energy metabolism. Rehabilitation of blood-brain barrier functionality and energy metabolism may secure the neuron from cell death.

    DOI: 10.3390/genes13122385

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  • A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching Reviewed International coauthorship International journal

    Y-h. Taguchi, Turki Turki

    Scientific Reports   12 ( 1 )   2022.12

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    Abstract

    The integrated analysis of multiple gene expression profiles previously measured in distinct studies is problematic since missing both sample matches and common labels prevent their integration in fully data-driven, unsupervised training. In this study, we propose a strategy to enable the integration of multiple gene expression profiles among multiple independent studies with neither labeling nor sample matching using tensor decomposition unsupervised feature extraction. We apply this strategy to Alzheimer’s disease (AD)-related gene expression profiles that lack precise correspondence among samples, including AD single-cell RNA sequence (scRNA-seq) data. We were able to select biologically reasonable genes using the integrated analysis. Overall, integrated gene expression profiles can function analogously to prior- and/or transfer-learning strategies in other machine-learning applications. For scRNA-seq, the proposed approach significantly reduces the required computational memory.

    DOI: 10.1038/s41598-022-25524-4

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    Other Link: https://www.nature.com/articles/s41598-022-25524-4

  • Editorial: microRNA Bioinformatics International journal

    Y-h. Taguchi

    Cells   11 ( 22 )   3677 - 3677   2022.11

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:MDPI AG  

    Firstly, I apologize for the delayed publication of this Special Issue in the form of a book title [...]

    DOI: 10.3390/cells11223677

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  • Adapted tensor decomposition and PCA based unsupervised feature extraction select more biologically reasonable differentially expressed genes than conventional methods Reviewed International coauthorship International journal

    Y-h. Taguchi, Turki Turki

    Scientific Reports   12 ( 1 )   17438 - 17438   2022.10

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    Abstract

    Tensor decomposition- and principal component analysis-based unsupervised feature extraction were proposed almost 5 and 10 years ago, respectively; although these methods have been successfully applied to a wide range of genome analyses, including drug repositioning, biomarker identification, and disease-causing genes’ identification, some fundamental problems have been identified: the number of genes identified was too small to assume that there were no false negatives, and the histogram of P values derived was not fully coincident with the null hypothesis that principal component and singular value vectors follow the Gaussian distribution. Optimizing the standard deviation such that the histogram of P values is as much as possible coincident with the null hypothesis results in an increase in the number and biological reliability of the selected genes. Our contribution was that we improved these methods so as to be able to select biologically more reasonable differentially expressed genes than the state of art methods that must empirically assume negative binomial distributions and dispersion relation, which is required for the selecting more expressed genes than less expressed ones, which can be achieved by the proposed methods that do not have to assume these.

    DOI: 10.1038/s41598-022-21474-z

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    Other Link: https://www.nature.com/articles/s41598-022-21474-z

  • Projection in genomic analysis: A theoretical basis to rationalize tensor decomposition and principal component analysis as feature selection tools Reviewed International coauthorship International journal

    Y-h. Taguchi, Turki Turki

    PLOS ONE   17 ( 9 )   e0275472 - e0275472   2022.9

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Public Library of Science (PLoS)  

    Identifying differentially expressed genes is difficult because of the small number of available samples compared with the large number of genes. Conventional gene selection methods employing statistical tests have the critical problem of heavy dependence of P-values on sample size. Although the recently proposed principal component analysis (PCA) and tensor decomposition (TD)-based unsupervised feature extraction (FE) has often outperformed these statistical test-based methods, the reason why they worked so well is unclear. In this study, we aim to understand this reason in the context of projection pursuit (PP) that was proposed a long time ago to solve the problem of dimensions; we can relate the space spanned by singular value vectors with that spanned by the optimal cluster centroids obtained from K-means. Thus, the success of PCA- and TD-based unsupervised FE can be understood by this equivalence. In addition to this, empirical threshold adjusted P-values of 0.01 assuming the null hypothesis that singular value vectors attributed to genes obey the Gaussian distribution empirically corresponds to threshold-adjusted P-values of 0.1 when the null distribution is generated by gene order shuffling. For this purpose, we newly applied PP to the three data sets to which PCA and TD based unsupervised FE were previously applied; these data sets treated two topics, biomarker identification for kidney cancers (the first two) and the drug discovery for COVID-19 (the thrid one). Then we found the coincidence between PP and PCA or TD based unsupervised FE is pretty well. Shuffling procedures described above are also successfully applied to these three data sets. These findings thus rationalize the success of PCA- and TD-based unsupervised FE for the first time.

    DOI: 10.1371/journal.pone.0275472

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  • Estimation of Metabolic Effects upon Cadmium Exposure during Pregnancy Using Tensor Decomposition Reviewed International journal

    Yuki Amakura, Y-h. Taguchi

    Genes   13 ( 10 )   1698 - 1698   2022.9

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    Authorship:Last author, Corresponding author   Publishing type:Research paper (scientific journal)   Publisher:MDPI AG  

    A simple tensor decomposition model was applied to the liver transcriptome analysis data to elucidate the cause of cadmium-induced gene overexpression. In addition, we estimated the mechanism by which prenatal Cd exposure disrupts insulin metabolism in offspring. Numerous studies have reported on the toxicity of Cd. A liver transcriptome analysis revealed that Cd toxicity induces intracellular oxidative stress and mitochondrial dysfunction via changes in gene expression, which in turn induces endoplasmic reticulum-associated degradation via abnormal protein folding. However, the specific mechanisms underlying these effects remain unknown. In this study, we found that Cd-induced endoplasmic reticulum stress may promote increased expression of tumor necrosis factor-α (TNF-α). Based on the high expression of genes involved in the production of sphingolipids, it was also found that the accumulation of ceramide may induce intracellular oxidative stress through the overproduction of reactive oxygen species. In addition, the high expression of a set of genes involved in the electron transfer system may contribute to oxidative stress. These findings allowed us to identify the mechanisms by which intracellular oxidative stress leads to the phosphorylation of insulin receptor substrate 1, which plays a significant role in the insulin signaling pathway.

    DOI: 10.3390/genes13101698

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  • Suppression of intrahepatic cholangiocarcinoma cell growth by <scp>SKI</scp> via upregulation of the CDK inhibitor p21 Reviewed International journal

    Etsushi Kawamura, Tsutomu Matsubara, Atsuko Daikoku, Sanae Deguchi, Masahiko Kinoshita, Hideto Yuasa, Hayato Urushima, Naoshi Odagiri, Hiroyuki Motoyama, Kohei Kotani, Ritsuzo Kozuka, Atsushi Hagihara, Hideki Fujii, Sawako Uchida‐Kobayashi, Shogo Tanaka, Shigekazu Takemura, Keiko Iwaisako, Masaru Enomoto, YH Taguchi, Akihiro Tamori, Shoji Kubo, Kazuo Ikeda, Norifumi Kawada

    FEBS Open Bio   12 ( 12 )   2122 - 2135   2022.9

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Wiley  

    Cholangiocarcinoma (CC) has a poor prognosis and different driver genes depending on the site of onset. Intrahepatic CC is the second-most common liver cancer after hepatocellular carcinoma, and novel therapeutic targets are urgently needed. The present study was conducted to identify novel therapeutic targets by exploring differentially regulated genes in human CC. MicroRNA (miRNA) and mRNA microarrays were performed using tissue and serum samples obtained from 24 surgically resected hepatobiliary tumor cases, including 10 CC cases. We conducted principal component analysis to identify differentially expressed miRNA, leading to the identification of miRNA-3648 as a differentially expressed miRNA. We used an in silico screening approach to identify its target mRNA, the tumor suppressor Sloan Kettering Institute (SKI). SKI protein expression was decreased in human CC cells overexpressing miRNA-3648, endogenous SKI protein expression was decreased in human CC tumor tissues, and endogenous SKI mRNA expression was suppressed in human CC cells characterized by rapid growth. SKI-overexpressing OZ cells (human intrahepatic CC cells) showed upregulation of cyclin-dependent kinase inhibitor p21 mRNA and protein expression and suppressed cell proliferation. Nuclear expression of CDT1 (chromatin licensing and DNA replication factor 1), which is required for the G1/S transition, was suppressed in SKI-overexpressing OZ cells. SKI knockdown resulted in the opposite effects. Transgenic p21-luciferase was activated in SKI-overexpressing OZ cells. These data indicate SKI involvement in p21 transcription and that SKI-p21 signaling causes cell cycle arrest in G1, suppressing intrahepatic CC cell growth. Therefore, SKI may be a potential therapeutic target for intrahepatic CC.

    DOI: 10.1002/2211-5463.13489

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  • Exploring plausible therapeutic targets for Alzheimer's disease using multi-omics approach, machine learning and docking Reviewed International coauthorship International journal

    M. Michael Gromiha, S. Akila Parvathy Dharshini, Nela Pragathi Sneha, Dhanusha Yesudhas, A. Kulandaisamy, Uday Rangaswamy, Anusuya Shanmugam, Y-h. Taguchi

    Current Topics in Medicinal Chemistry   22   2022.9

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Bentham Science Publishers Ltd.  

    Abstract:

    The progressive deterioration of neurons leads to Alzheimer's disease (AD), and developing a drug for this disorder is challenging. Substantial gene/transcriptome variability from multiple cell types leads to downstream pathophysiologic consequences that represent the heterogeneity of this disease. Identifying potential biomarkers for promising therapeutics is strenuous due to the fact that the transcriptome, epigenetic, or proteome changes detected in patients are not clear whether they are the cause or consequence of the disease, which eventually makes the drug discovery efforts intricate. The advancement in scRNA-sequencing technologies helps to identify cell type-specific biomarkers that may guide the selection of the pathways and related targets specific to different stages of the disease progression. This review is focussed on the analysis of multi-omics data from various perspectives (genomic and transcriptomic variants, and single-cell expression), which provide insights to identify plausible molecular targets to combat this complex disease. Further, we briefly outlined the developments in machine learning techniques to prioritize the risk-associated genes, predict probable mutations and identify promising drug candidates from natural products.

    DOI: 10.2174/1568026622666220902110115

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  • RNA m6A modification and microRNAs

    Y.-H. Taguchi

    MicroRNA   169 - 180   2022.7

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  • In Silico Drug Discovery Using Tensor Decomposition Based Unsupervised Feature Extraction

    Y.-H. Taguchi

    Studies in Big Data   101 - 120   2022.6

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    DOI: 10.1007/978-981-16-9158-4_7

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  • Sincle Cell RNA-seq Analysis Using Tensor Decomposition and Principal Component Analysis Based Unsupervised Feature Extraction

    Y.-H. Taguchi

    Studies in Big Data   155 - 176   2022.6

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    DOI: 10.1007/978-981-16-9158-4_11

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  • Multiomics Data Analysis of Cancers Using Tensor Decomposition and Principal Component Analysis Based Unsupervised Feature Extraction

    Y.-H. Taguchi

    Studies in Big Data   1 - 17   2022.6

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    DOI: 10.1007/978-981-16-9158-4_1

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  • Handbook of Machine Learning Applications for Genomics

    Y-h. Taguchi

    2022.6

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    DOI: 10.1007/978-981-16-9158-4

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  • Integrated Analysis of Tissue-Specific Gene Expression in Diabetes by Tensor Decomposition Can Identify Possible Associated Diseases Reviewed International coauthorship International journal

    Y-H. Taguchi, Turki Turki

    Genes   13 ( 6 )   1097 - 1097   2022.6

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    In the field of gene expression analysis, methods of integrating multiple gene expression profiles are still being developed and the existing methods have scope for improvement. The previously proposed tensor decomposition-based unsupervised feature extraction method was improved by introducing standard deviation optimization. The improved method was applied to perform an integrated analysis of three tissue-specific gene expression profiles (namely, adipose, muscle, and liver) for diabetes mellitus, and the results showed that it can detect diseases that are associated with diabetes (e.g., neurodegenerative diseases) but that cannot be predicted by individual tissue expression analyses using state-of-the-art methods. Although the selected genes differed from those identified by the individual tissue analyses, the selected genes are known to be expressed in all three tissues. Thus, compared with individual tissue analyses, an integrated analysis can provide more in-depth data and identify additional factors, namely, the association with other diseases.

    DOI: 10.3390/genes13061097

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  • Tensor Decomposition and Principal Component Analysis-Based Unsupervised Feature Extraction Outperforms State-of-the-Art Methods When Applied to Histone Modification Profiles

    Sanjiban Sekhar Roy, Y-h. Taguchi

    2022.5

  • Tumor Heterogeneity and Molecular Characteristics of Glioblastoma Revealed by Single-Cell RNA-Seq Data Analysis Reviewed International coauthorship International journal

    Yesudhas, D, Dharshini, S.A.P, Taguchi, Y.-h, Gromiha, M.M

    Genes   13 ( 3 )   428 - 428   2022.2

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    Glioblastoma multiforme (GBM) is the most common infiltrating lethal tumor of the brain. Tumor heterogeneity and the precise characterization of GBM remain challenging, and the disease-specific and effective biomarkers are not available at present. To understand GBM heterogeneity and the disease prognosis mechanism, we carried out a single-cell transcriptome data analysis of 3389 cells from four primary IDH-WT (isocitrate dehydrogenase wild type) glioblastoma patients and compared the characteristic features of the tumor and periphery cells. We observed that the marker gene expression profiles of different cell types and the copy number variations (CNVs) are heterogeneous in the GBM samples. Further, we have identified 94 differentially expressed genes (DEGs) between tumor and periphery cells. We constructed a tissue-specific co-expression network and protein–protein interaction network for the DEGs and identified several hub genes, including CX3CR1, GAPDH, FN1, PDGFRA, HTRA1, ANXA2 THBS1, GFAP, PTN, TNC, and VIM. The DEGs were significantly enriched with proliferation and migration pathways related to glioblastoma. Additionally, we were able to identify the differentiation state of microglia and changes in the transcriptome in the presence of glioblastoma that might support tumor growth. This study provides insights into GBM heterogeneity and suggests novel potential disease-specific biomarkers which could help to identify the therapeutic targets in GBM.

    DOI: 10.3390/genes13030428

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  • Novel feature selection method via kernel tensor decomposition for improved multi-omics data analysis Reviewed International coauthorship International journal

    Y-h. Taguchi, Turki Turki

    BMC Medical Genomics   15 ( 1 )   37   2022.2

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    Background
    Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately 102–105 features. In particular, appropriate methods to weight individual omics datasets are unclear, and the approach adopted has substantial consequences for feature selection. In this study, we extended a recently proposed kernel tensor decomposition (KTD)-based unsupervised feature extraction (FE) method to integrate multi-omics datasets obtained from common samples in a weight-free manner.

    Method
    KTD-based unsupervised FE was reformatted as the collection of kernelized tensors sharing common samples, which was applied to synthetic and real datasets.

    Results
    The proposed advanced KTD-based unsupervised FE method showed comparative performance to that of the previously proposed KTD method, as well as tensor decomposition-based unsupervised FE, but required reduced memory and central processing unit time. Moreover, this advanced KTD method, specifically designed for multi-omics analysis, attributes P values to features, which is rare for existing multi-omics–oriented methods.

    Conclusions
    The sample R code is available at https://github.com/tagtag/MultiR/.

    DOI: 10.1186/s12920-022-01181-4

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  • Effects of Collagen–Glycosaminoglycan Mesh on Gene Expression as Determined by Using Principal Component Analysis-Based Unsupervised Feature Extraction Reviewed International coauthorship International journal

    Y-h. Taguchi, Turki Turki

    Polymers   13 ( 23 )   4117 - 4117   2021.11

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    The development of the medical applications for substances or materials that contact cells is important. Hence, it is necessary to elucidate how substances that surround cells affect gene expression during incubation. In the current study, we compared the gene expression profiles of cell lines that were in contact with collagen–glycosaminoglycan mesh and control cells. Principal component analysis-based unsupervised feature extraction was applied to identify genes with altered expression during incubation in the treated cell lines but not in the controls. The identified genes were enriched in various biological terms. Our method also outperformed a conventional methodology, namely, gene selection based on linear regression with time course.

    DOI: 10.3390/polym13234117

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  • End-to-End Deep Learning for Detecting Metastatic Breast Cancer in Axillary Lymph Node from Digital Pathology Images Reviewed International coauthorship International journal

    Turki Turki, Anmar Al-Sharif, Y-h. Taguchi

    Intelligent Data Engineering and Automated Learning – IDEAL 2021   343 - 353   2021.11

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    DOI: 10.1007/978-3-030-91608-4_34

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  • Identification of Enhancers and Promoters in the Genome by Multidimensional Scaling Reviewed International journal

    Ryo Ishibashi, Y-h. Taguchi

    Genes   12 ( 11 )   1671 - 1671   2021.10

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    The positions of enhancers and promoters on genomic DNA remain poorly understood. Chromosomes cannot be observed during the cell division cycle because the genome forms a chromatin structure and spreads within the nucleus. However, high-throughput chromosome conformation capture (Hi-C) measures the physical interactions of genomes. In previous studies, DNA extrusion loops were directly derived from Hi-C heat maps. Multidimensional Scaling (MDS) is used in this assessment to more precisely locate enhancers and promoters. MDS is a multivariate analysis method that reproduces the original coordinates from the distance matrix between elements. We used Hi-C data of cultured osteosarcoma cells and applied MDS as the distance matrix of the genome. In addition, we selected columns 2 and 3 of the orthogonal matrix U as the desired structure. Overall, the DNA loops from the reconstructed genome structure contained bioprocesses involved in transcription, such as the pre-transcriptional initiation complex and RNA polymerase II initiation complex, and transcription factors involved in cancer, such as Foxm1 and CREB3. Therefore, our results are consistent with the biological findings. Our method is suitable for identifying enhancers and promoters in the genome.

    DOI: 10.3390/genes12111671

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  • Tensor-Decomposition-Based Unsupervised Feature Extraction in Single-Cell Multiomics Data Analysis Reviewed International coauthorship International journal

    Y-H. Taguchi, Turki Turki

    Genes   12 ( 9 )   1442 - 1442   2021.9

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    DOI: 10.3390/genes12091442

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  • PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients Reviewed International journal

    Kota Fujisawa, Mamoru Shimo, Y.-H. Taguchi, Shinya Ikematsu, Ryota Miyata

    Scientific Reports   11 ( 1 )   2021.8

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    <title>Abstract</title>Coronavirus disease 2019 (COVID-19) is raging worldwide. This potentially fatal infectious disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the complete mechanism of COVID-19 is not well understood. Therefore, we analyzed gene expression profiles of COVID-19 patients to identify disease-related genes through an innovative machine learning method that enables a data-driven strategy for gene selection from a data set with a small number of samples and many candidates. Principal-component-analysis-based unsupervised feature extraction (PCAUFE) was applied to the RNA expression profiles of 16 COVID-19 patients and 18 healthy control subjects. The results identified 123 genes as critical for COVID-19 progression from 60,683 candidate probes, including immune-related genes. The 123 genes were enriched in binding sites for transcription factors NFKB1 and RELA, which are involved in various biological phenomena such as immune response and cell survival: the primary mediator of canonical nuclear
    factor-kappa B (NF-<italic>κ</italic>B) activity is the heterodimer RelA-p50. The genes were also enriched in histone modification H3K36me3, and they largely overlapped the target genes of NFKB1 and RELA. We found that the overlapping genes were downregulated in COVID-19 patients. These results suggest that canonical NF-<italic>κ</italic>B activity was suppressed by H3K36me3 in COVID-19 patient blood.

    DOI: 10.1038/s41598-021-95698-w

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  • RNA-Seq data analysis for Planarian with tensor decomposition-based unsupervised feature extraction

    Makoto Kashima, Nobuyoshi Kumagai, Hiromi Hirata, Y-h. Taguchi

    2021.6

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    Publishing type:Research paper (scientific journal)   Publisher:Cold Spring Harbor Laboratory  

    DOI: 10.1101/2021.06.15.448531

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  • Unsupervised tensor decomposition-based method to extract candidate transcription factors as histone modification bookmarks in post-mitotic transcriptional reactivation Reviewed International coauthorship International journal

    Y-h. Taguchi, Turki Turki

    PLOS ONE   16 ( 5 )   e0251032 - e0251032   2021.5

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    The histone group added to a gene sequence must be removed during mitosis to halt transcription during the DNA replication stage of the cell cycle. However, the detailed mechanism of this transcription regulation remains unclear. In particular, it is not realistic to reconstruct all appropriate histone modifications throughout the genome from scratch after mitosis. Thus, it is reasonable to assume that there might be a type of “bookmark” that retains the positions of histone modifications, which can be readily restored after mitosis. We developed a novel computational approach comprising tensor decomposition (TD)-based unsupervised feature extraction (FE) to identify transcription factors (TFs) that bind to genes associated with reactivated histone modifications as candidate histone bookmarks. To the best of our knowledge, this is the first application of TD-based unsupervised FE to the cell division context and phases pertaining to the cell cycle in general. The candidate TFs identified with this approach were functionally related to cell division, suggesting the suitability of this method and the potential of the identified TFs as bookmarks for histone modification during mitosis.

    DOI: 10.1371/journal.pone.0251032

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  • Novel method for the prediction of drug-drug Interaction based on Gene Expression profiles Reviewed International coauthorship International journal

    Yh. Taguchi, Turki Turki

    European Journal of Pharmaceutical Sciences   160   105742 - 105742   2021.5

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    DOI: 10.1016/j.ejps.2021.105742

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  • Discriminating the Single-cell Gene Regulatory Networks of Human Pancreatic Islets: A Novel Deep Learning Application Reviewed International coauthorship International journal

    Turki Turki, Y-h. Taguchi

    Computers in Biology and Medicine   132   104257 - 104257   2021.5

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    DOI: 10.1016/j.compbiomed.2021.104257

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  • Identification of genes associated with altered gene expression and m6A profiles during hypoxia using tensor decomposition based unsupervised feature extraction Reviewed International coauthorship International journal

    Sanjiban Sekhar Roy, Y.-H. Taguchi

    Scientific Reports   11 ( 1 )   8909   2021.4

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    <title>Abstract</title>Although hypoxia is a critical factor that can drive the progression of various diseases, the mechanism underlying hypoxia itself remains unclear. Recently, m6A has been proposed as an important factor driving hypoxia. Despite successful analyses, potential genes were not selected with statistical significance but were selected based solely on fold changes. Because the number of genes is large while the number of samples is small, it was impossible to select genes using conventional feature selection methods with statistical significance. In this study, we applied the recently proposed principal component analysis (PCA), tensor decomposition (TD), and kernel tensor decomposition (KTD)-based unsupervised feature extraction (FE) to a hypoxia data set. We found that PCA, TD, and KTD-based unsupervised FE could successfully identify a limited number of genes associated with altered gene expression and m6A profiles, as well as the enrichment of hypoxia-related biological terms, with improved statistical significance.

    DOI: 10.1038/s41598-021-87779-7

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  • Editorial: miRNAs and Neurological Diseases International coauthorship International journal

    Hsiuying Wang, Y. H. Taguchi, Xianshuang Liu

    Frontiers in Neurology   12   2021.4

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    DOI: 10.3389/fneur.2021.662373

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  • Mathematical formulation and application of kernel tensor decomposition based unsupervised feature extraction Reviewed International coauthorship International journal

    Y-h. Taguchi, Turki Turki

    Knowledge-Based Systems   217   106834 - 106834   2021.4

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    DOI: 10.1016/j.knosys.2021.106834

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  • Application of Tensor Decomposition to Gene Expression of Infection of Mouse Hepatitis Virus Can Identify Critical Human Genes and Efffective Drugs for SARS-CoV-2 Infection Reviewed International coauthorship International journal

    Y-H. Taguchi, Turki Turki

    IEEE Journal of Selected Topics in Signal Processing   15 ( 3 )   746 - 758   2021.4

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    Authorship:Lead author, Corresponding author   Publishing type:Research paper (scientific journal)   Publisher:Institute of Electrical and Electronics Engineers (IEEE)  

    DOI: 10.1109/jstsp.2021.3061251

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  • Exploring common therapeutic targets for neurodegenerative disorders using transcriptome study Reviewed International coauthorship International journal

    Akila Ranjith, Sherlyn Jemimah, Y-h. Taguchi, Michael Gromiha

    Frontiers in Genetics: RNA   12   639160   2021.3

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    DOI: 10.3389/fgene.2021.639160

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  • Identification of Transcription Factors, Biological Pathways, and Diseases as Mediated by N6-methyladenosine Using Tensor Decomposition-Based Unsupervised Feature Extraction Reviewed International coauthorship International journal

    Y-h. Taguchi, S. Akila Parvathy Dharshini, M. Michael Gromiha

    Applied Sciences   11 ( 1 )   213 - 213   2020.12

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    N6-methyladenosine (m6A) editing is the most common RNA modification known to contribute to various biological processes. Nevertheless, the mechanism by which m6A regulates transcription is unclear. Recently, it was proposed that m6A controls transcription through histone modification, although no comprehensive analysis using this dataset was performed. In this study, we applied tensor decomposition (TD)-based unsupervised feature extraction (FE) to a dataset composed of mouse embryonic stem cells (mESC) and a human cancer cell line (HEC-1-A) and successfully identified two sets of genes significantly overlapping between humans and mice (63 significantly overlapped genes among a total of 16,763 genes common to the two species). These significantly overlapped genes occupy at most 10% genes from both gene sets. Using these two sets of genes, we identified transcription factors (TFs) that m6A might recruit, biological processes that m6A might contribute to, and diseases that m6A might cause; they also largely overlap with each other. Since they were commonly identified using two independent datasets, the results regarding these TFs, biological processes, and diseases should be highly robust and trustworthy. It will help us to understand the mechanisms by which m6A contributes to biological processes.

    DOI: 10.3390/app11010213

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  • Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data Reviewed International coauthorship International journal

    Y-h. Taguchi, Turki Turki

    Genes   11 ( 12 )   1493 - 1493   2020.12

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    The large p small n problem is a challenge without a de facto standard method available to it. In this study, we propose a tensor-decomposition (TD)-based unsupervised feature extraction (FE) formalism applied to multiomics datasets, in which the number of features is more than 100,000 whereas the number of samples is as small as about 100, hence constituting a typical large p small n problem. The proposed TD-based unsupervised FE outperformed other conventional supervised feature selection methods, random forest, categorical regression (also known as analysis of variance, or ANOVA), penalized linear discriminant analysis, and two unsupervised methods, multiple non-negative matrix factorization and principal component analysis (PCA) based unsupervised FE when applied to synthetic datasets and four methods other than PCA based unsupervised FE when applied to multiomics datasets. The genes selected by TD-based unsupervised FE were enriched in genes known to be related to tissues and transcription factors measured. TD-based unsupervised FE was demonstrated to be not only the superior feature selection method but also the method that can select biologically reliable genes. To our knowledge, this is the first study in which TD-based unsupervised FE has been successfully applied to the integration of this variety of multiomics measurements.

    DOI: 10.3390/genes11121493

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  • Comprehensive analysis of liver and blood miRNA in precancerous conditions Reviewed International journal

    Tomohiro Umezu, Koichi Tsuneyama, Kohsuke Kanekura, Michiyo Hayakawa, Toshihito Tanahashi, Mitsuoki Kawano, Y-h Taguchi, Hidenori Toyoda, Akihiro Tamori, Masahiko Kuroda, Yoshiki Murakami

    Scientific Reports   10 ( 1 )   21766 - 21766   2020.12

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    <title>Abstract</title>Streptozotocin administration to mice (STZ-mice) induces type I diabetes and hepatocellular carcinoma (HCC). We attempted to elucidate the carcinogenic mechanism and the miRNA expression status in the liver and blood during the precancerous state. Serum and liver tissues were collected from STZ-mice and non-treated mice (CTL-mice) at 6, 10, and 12 W. The exosome enriched fraction extracted from serum was used. Hepatic histological examination and hepatic and exosomal miRNA expression analysis were serially performed using next-generation sequencing (NGS). Human miRNA expression analysis of chronic hepatitis liver tissue and exosomes, which were collected before starting the antiviral treatment, were also performed. No inflammation or fibrosis was found in the liver of CTL-mice during the observation period. In STZ-mice, regeneration and inflammation of hepatocytes was found at 6 W and nodules of atypical hepatocytes were found at 10 and 12 W. In the liver tissue, during 6–12 W, the expression levels of let-7f-5p, miR-143-3p, 148a-3p, 191-5p, 192-5p, 21a-5p, 22-3p, 26a-5p, and 92a-3p was significantly increased in STZ-mice, and anti-oncogenes of their target gene candidates were down-regulated. miR-122-5p was also significantly down-regulated in STZ-mice. Fifteen exosomal miRNAs were upregulated in STZ-mice. Six miRNAs (let-7f-5p, miR-10b-5p, 143-3p, 191-5p, 21a-5p, and 26a-5p) were upregulated, similarly to human HCC cases. From the precancerous state, aberrant expression of hepatic miRNAs has already occurred, and then, it can promote carcinogenesis. In exosomes, the expression pattern of common miRNAs between mice and humans before carcinogenesis was observed and can be expected to be developed as a cancer predictive marker.

    DOI: 10.1038/s41598-020-78500-1

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  • Developing a diagnostic method for latent tuberculosis infection using circulating miRNA Reviewed International journal

    Shoji Hashimoto, Hong Zhao, Michiyo Hayakawa, Koichi Nakajima, Y-h Taguchi, Yoshiki Murakami

    Translational Medicine Communications   5 ( 1 )   25   2020.12

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    <title>Abstract</title><sec>
    <title>Background</title>
    <italic>Mycobacterium tuberculosis</italic> is known to cause latent tuberculosis infection (LTBI) in 25–50% of the cases, of whom 10–20% develop active tuberculosis (TB). Notably, no marker currently exists for judging the therapeutic effect of TB; it is currently judged by chest X-ray and clinical symptoms. We attempted to establish a marker for distinguishing LTBI from active TB and to identify the probability of recurrence after TB treatment, using information on circulating miRNA expression.


    </sec><sec>
    <title>Methods</title>
    In total, 32 patients were enrolled in this study: 16 with an onset or recurrence of active TB, and 16 with LTBI showing positive interferon-gamma release assays (IGRA) test and chest X-ray. Total RNA from serum in an exosome-rich fraction was first extracted, followed by miRNA expression analysis using a next-generation sequencer, then, this data were analyzed using miRDeep2.


    </sec><sec>
    <title>Results</title>
    Using the expression information of eight miRNAs, LTBI and TB could be diagnosed with an accuracy of 71.8% (odds ratio: 6.16, <italic>p value</italic> = 3.20e-02).


    </sec><sec>
    <title>Conclusions</title>
    A novel method for efficiently differentiating between LTBI and active TB was established. This method appears to be promising for evaluating the therapeutic effect of TB, as it can be performed in a minimally invasive manner.


    </sec>

    DOI: 10.1186/s41231-020-00078-7

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  • Identification of miRNA signatures for kidney renal clear cell carcinoma using the tensor-decomposition method Reviewed International coauthorship International journal

    Ka-Lok Ng, Y-H. Taguchi

    Scientific Reports   10   15149   2020.9

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    DOI: 10.1038/s41598-020-71997-6

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  • A new advanced in silico drug discovery method for novel coronavirus (SARS-CoV-2) with tensor decomposition-based unsupervised feature extraction Reviewed International coauthorship International journal

    Y-h. Taguchi, Turki Turki

    PLOS ONE   15 ( 9 )   e0238907   2020.9

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    DOI: 10.1371/journal.pone.0238907

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  • Universal Nature of Drug Treatment Responses in Drug-Tissue-Wide Model-Animal Experiments Using Tensor Decomposition-Based Unsupervised Feature Extraction Reviewed International coauthorship International journal

    Yh. Taguchi, Turki Turki

    Frontiers in Genetics   11   695   2020.8

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    Gene expression profiles of tissues treated with drugs have recently been used to infer clinical outcomes. Although this method is often successful from the application point of view, gene expression altered by drugs is rarely analyzed in detail, because of the extremely large number of genes involved. Here, we applied tensor decomposition (TD)-based unsupervised feature extraction (FE) to the gene expression profiles of 24 mouse tissues treated with 15 drugs. TD-based unsupervised FE enabled identification of the common effects of 15 drugs including an interesting universal feature: these drugs affect genes in a gene-group-wide manner and were dependent on three tissue types (neuronal, muscular, and gastroenterological). For each tissue group, TD-based unsupervised FE enabled identification of a few tens to a few hundreds of genes affected by the drug treatment. These genes are distinctly expressed between drug treatments and controls as well as between tissues in individual tissue groups and other tissues. We also validated the assignment of genes to individual tissue groups using multiple enrichment analyses. We conclude that TD-based unsupervised FE is a promising method for integrated analysis of gene expression profiles from multiple tissues treated with multiple drugs in a completely unsupervised manner.

    DOI: 10.3389/fgene.2020.00695

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  • Incremental Dilations Using CNN for Brain Tumor Classification Reviewed International coauthorship International journal

    Roy, S.S, Rodrigues, N, Taguchi, Y.-H

    Applied Sciences   10 ( 14 )   4915 - 4915   2020.7

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    Brain tumor classification is a challenging task in the field of medical image processing.
    Technology has now enabled medical doctors to have additional aid for diagnosis. We aim to classify brain tumors using MRI images, which were collected from anonymous patients and artificial brain simulators . In this article, we carry out a comparative study between Simple Artificial Neural Networks with dropout, Basic Convolutional Neural Networks (CNN), and Dilated Convolutional Neural Networks. The experimental results shed light on the high classification performance (accuracy 97%) of Dilated CNN. On the other hand, Dilated CNN suffers from the gridding phenomenon. An incremental, even number dilation rate takes advantage of the reduced computational overhead and also overcomes the adverse effects of gridding. Comparative analysis between different combinations of dilation rates for the different convolution layers, help validate
    the results. The computational overhead in terms of efficiency for training the model to reach an acceptable threshold accuracy of 90% is another parameter to compare the model performance.

    DOI: 10.3390/app10144915

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  • Identifying suitable tools for variant detection and differential gene expression using RNA-seq data Reviewed International coauthorship International journal

    S. Akila, Parvathy Dharshini, Y-h. TAGUCHI, M. Michael Gromiha

    Genomics   112 ( 3 )   2166 - 2172   2020.5

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    DOI: 10.1016/j.ygeno.2019.12.011

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  • Author Correction: Development of a novel anti-hepatitis B virus agent via Sp1. Reviewed International journal

    Michiyo Hayakawa, Hideaki Umeyama, Mitsuo Iwadate, Y-H Taguchi, Yoshihiko Yano, Takashi Honda, Saori Itami-Matsumoto, Ritsuzo Kozuka, Masaru Enomoto, Akihiro Tamori, Norifumi Kawada, Yoshiki Murakami

    Scientific reports   10 ( 1 )   7015 - 7015   2020.4

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    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

    DOI: 10.1038/s41598-020-63866-z

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  • SCGRNs: Novel supervised inference of single-cell gene regulatory networks of complex diseases Reviewed International coauthorship International journal

    Turki Turki, Y-h.Taguchi

    Computers in Biology and Medicine   118   103656   2020.3

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  • Development of a novel anti-hepatitis B virus agent via Sp1 Reviewed International journal

    Michiyo Hayakawa, Hideaki Umeyama, Mitsuo Iwadate, Y.-H. Taguchi, Yoshihiko Yano, Takashi Honda, Saori Itami-Matsumoto, Ritsuzo Kozuka, Masaru Enomoto, Akihiro Tamori, Norifumi Kawada, Yoshiki Murakami

    Scientific Reports   10 ( 1 )   47 - 47   2020.1

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    Nucleos(t)ide analog (NA) therapy has proven effective in treating chronic hepatitis B. However, NAs frequently result in viral relapse after the cessation of therapy. This is because NAs cannot fully eliminate the viral episomal covalently closed circular DNA (cccDNA) in the nucleus. In this study, we identified small molecular compounds that control host factors related to viral replication using in silico screening with simulated annealing based on bioinformatics for protein-ligand flexible docking. Twelve chemical compound candidates for alpha-glucosidase (AG) inhibitors were identified from a library of chemical compounds and used to treat fresh human hepatocytes infected with HBV. They were then monitored for their anti-viral effects. HBV replication was inhibited by one candidate (1-[3-(4-tert-butylcyclohexyl)oxy-2-hydroxypropyl]-2,2,6,6-tetramethylpiperidin-4-ol) in a dose-dependent manner. This compound significantly reduced ccc DNA production, compared to Entecavir (p < 0.05), and had a lower anti-AG effect. Gene expression analysis and structural analysis of this compound showed that its inhibitive effect on HBV was via interaction with Sp1. The nuclear transcription factor Sp1 acts on multiple regions of HBV to suppress HBV replication. Identifying candidates that control nuclear transcription factors facilitate the development of novel therapies. Drugs with a mechanism different from NA are promising for the elimination of HBV.

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  • Novel Method for Detection of Genes With Altered Expression Caused by Coronavirus Infection and Screening of Candidate Drugs for SARS-CoV-2

    Y-h. Taguchi

    2020

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    DOI: 10.20944/PREPRINTS202004.0431.V2

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  • Novel Method for Detection of Genes With Altered Expression Caused by Coronavirus Infection and Screening of Candidate Drugs for SARS-CoV-2

    Y-h. Taguchi

    Preprints.org   2020

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  • Neurological disorder drug discovery from gene expression with tensor decomposition Reviewed International coauthorship International journal

    Y-h. Taguchi, Turki Turki

    Current Pharmaceutical Design   25 ( 43 )   4588 - 4598   2019.12

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    DOI: 10.2174/1381612825666191210160906

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  • Investigating the energy crisis in Alzheimer disease using transcriptome study Reviewed International coauthorship International journal

    Dharshini, S.A.P, Taguchi, Y-h, Gromiha, M.M

    Scientific Reports   9   18509   2019.12

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    DOI: 10.1038/s41598-019-54782-y

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  • Tensor Decomposition Based Unsupervised Feature Extraction Applied to Bioinformatics

    Y-h. TAGUCHI

    APPLICATION OF OMICS, AI AND BLOCKCHAIN IN BIOINFORMATICS RESEARCH   159 - 188   2019.11

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    DOI: 10.1142/9789811203589_0010

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  • Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis Reviewed International coauthorship International journal

    Y-h. Taguchi, Turki Turki

    Frontiers in Genetics   10   864 - 864   2019.9

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    DOI: 10.3389/fgene.2019.00864

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  • Discovery of a Robust Gene Regulatory Network with a Complex Transcription Factor Network on Organ Cancer Cell-line RNA Sequence Data Reviewed

    Bharata Kalbuaji, Y-H. Taguchi, Akihiko Konagaya

    Chem-Bio Informatics Journal   19 ( 0 )   32 - 55   2019.9

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    DOI: 10.1273/cbij.19.32

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  • Comparative Transcriptomics Analysis Reviewed International journal

    Y.-h. Taguchi

    Encyclopedia of Bioinformatics and Computational Biology   3   814 - 818   2019.8

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    DOI: 10.1016/b978-0-12-809633-8.20163-5

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  • Regulation of Gene Expression Reviewed International journal

    Y.-h. Taguchi

    Encyclopedia of Bioinformatics and Computational Biology   3   806 - 813   2019.8

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    DOI: 10.1016/b978-0-12-809633-8.20667-5

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  • Machine Learning Algorithms for Predicting Drugs–Tissues Relationships Reviewed International coauthorship International journal

    Turki Turki, Y-h. TAGUCHI

    Expert Systems with Applications   127 ( 1 )   167 - 186   2019.8

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    DOI: 10.1016/j.eswa.2019.02.013

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  • Multiomics Data Analysis Using Tensor Decomposition Based Unsupervised Feature Extraction –Comparison with DIABLO– Reviewed

    Y-h. TAGUCHI

    Intelligent Computing Theories and Application   565 - 574   2019.7

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    DOI: 10.1007/978-3-030-26763-6_54

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  • Regulatory microRNA

    Y-h. Taguchi

    2019.4

  • Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data BMC Bioinformatics Reviewed International journal

    Y-h. Taguchi

    BMC Bioinformatics   19 ( S13 )   388   2019.2

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    DOI: 10.1186/s12859-018-2395-8

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  • Exploring MicroRNA Biomarkers for Parkinson’s Disease from mRNA Expression Profiles Reviewed International coauthorship International journal

    Y-h. TAGUCHI, Hsiuying Wang

    Cells   7 ( 12 )   245 - 245   2018.12

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    DOI: 10.3390/cells7120245

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  • Tensor decomposition--based unsupervised feature extraction for integrated analysis of TCGA data on microRNA expression and promoter methylation of genes in ovarian cancer Reviewed International coauthorship International journal

    Y-h Taguchi, Ka-Lok Ng

    2018 IEEE 17th International Conference on Bioinformatics and Bioengineeing   195 - 200   2018.11

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    DOI: 10.1109/BIBE.2018.00045

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  • Principal Component Analysis-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis Reviewed

    Y-h. Taguchi

    Intelligent Computing Theories and Application   816 - 826   2018.8

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    DOI: 10.1007/978-3-319-95933-7_90

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  • Collaborative environmental DNA sampling from petal surfaces of flowering cherry Cerasus × yedoensis 'Somei-yoshino' across the Japanese archipelago. Reviewed International journal

    Tazro Ohta, Takeshi Kawashima, Natsuko O Shinozaki, Akito Dobashi, Satoshi Hiraoka, Tatsuhiko Hoshino, Keiichi Kanno, Takafumi Kataoka, Shuichi Kawashima, Motomu Matsui, Wataru Nemoto, Suguru Nishijima, Natsuki Suganuma, Haruo Suzuki, Y-H Taguchi, Yoichi Takenaka, Yosuke Tanigawa, Momoka Tsuneyoshi, Kazutoshi Yoshitake, Yukuto Sato, Riu Yamashita, Kazuharu Arakawa, Wataru Iwasaki

    Journal of plant research   131 ( 4 )   709 - 717   2018.7

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    Recent studies have shown that environmental DNA is found almost everywhere. Flower petal surfaces are an attractive tissue to use for investigation of the dispersal of environmental DNA in nature as they are isolated from the external environment until the bud opens and only then can the petal surface accumulate environmental DNA. Here, we performed a crowdsourced experiment, the "Ohanami Project", to obtain environmental DNA samples from petal surfaces of Cerasus × yedoensis 'Somei-yoshino' across the Japanese archipelago during spring 2015. C. × yedoensis is the most popular garden cherry species in Japan and clones of this cultivar bloom simultaneously every spring. Data collection spanned almost every prefecture and totaled 577 DNA samples from 149 collaborators. Preliminary amplicon-sequencing analysis showed the rapid attachment of environmental DNA onto the petal surfaces. Notably, we found DNA of other common plant species in samples obtained from a wide distribution; this DNA likely originated from the pollen of the Japanese cedar. Our analysis supports our belief that petal surfaces after blossoming are a promising target to reveal the dynamics of environmental DNA in nature. The success of our experiment also shows that crowdsourced environmental DNA analyses have considerable value in ecological studies.

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  • Correction: Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing. Reviewed

    Y-h. Taguchi

    PloS one   2018.7

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  • Tensor Decomposition-Based Unsupervised Feature Extraction Can Identify the Universal Nature of Sequence-Nonspecific Off-Target Regulation of mRNA Mediated by MicroRNA Transfection Reviewed International journal

    Y.-H. Taguchi

    Cells   7 ( 6 )   54 - 54   2018.6

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  • Tensor decomposition-based and principal-component-analysis-based unsupervised feature extraction applied to the gene expression and methylation profiles in the brains of social insects with multiple castes Reviewed International journal

    Y. H. Taguchi

    BMC Bioinformatics   19 ( S4 )   99   2018.5

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    Background: Even though coexistence of multiple phenotypes sharing the same genomic background is interesting, it remains incompletely understood. Epigenomic profiles may represent key factors, with unknown contributions to the development of multiple phenotypes, and social-insect castes are a good model for elucidation of the underlying mechanisms. Nonetheless, previous studies have failed to identify genes associated with aberrant gene expression and methylation profiles because of the lack of suitable methodology that can address this problem properly. Methods: A recently proposed principal component analysis (PCA)-based and tensor decomposition (TD)-based unsupervised feature extraction (FE) can solve this problem because these two approaches can deal with gene expression and methylation profiles even when a small number of samples is available. Results: PCA-based and TD-based unsupervised FE methods were applied to the analysis of gene expression and methylation profiles in the brains of two social insects, Polistes canadensis and Dinoponera quadriceps. Genes associated with differential expression and methylation between castes were identified, and analysis of enrichment of Gene Ontology terms confirmed reliability of the obtained sets of genes from the biological standpoint. Conclusions: Biologically relevant genes, shown to be associated with significant differential gene expression and methylation between castes, were identified here for the first time. The identification of these genes may help understand the mechanisms underlying epigenetic control of development of multiple phenotypes under the same genomic conditions.

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  • Exploring microRNA biomarker for amyotrophic lateral sclerosis Reviewed International coauthorship International journal

    Y. H. Taguchi, Hsiuying Wang

    International Journal of Molecular Sciences   19 ( 5 )   1318   2018.5

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    Amyotrophic lateral sclerosis (ALS) is among the severe neuro degenerative diseases that lack widely available effective treatments. As the disease progresses, patients lose the control of voluntary muscles. Although the neuronal degeneration is the cause of this disease, the failure mechanism is still unknown. In order to seek genetic mechanisms that initiate and progress ALS, the association of microRNA (miRNA) expression with this disease was considered. Serum miRNAs from healthy controls, sporadic ALS (sALS), familial ALS (fALS) and ALS mutation carriers were investigated. Principal component analysis (PCA)-based unsupervised feature extraction (FE) was applied to these serum miRNA profiles. As a result, we predict miRNAs that can discriminate patients from healthy controls with high accuracy. Thus, these miRNAs can be potential prognosis miRNA biomarkers for ALS.

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  • Exploring the selective vulnerability in Alzheimer disease using tissue specific variant analysis Reviewed International coauthorship International journal

    S. Akila Parvathy Dharshini, Y. H. Taguchi, M. Michael Gromiha

    Genomics   111 ( 4 )   936 - 949   2018

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    The selective vulnerability of distinct regions of the brain is a critical factor in neurodegenerative disorders. In Alzheimer's disease (AD), neurons in hippocampus situated in medial temporal lobe are immensely damaged. Identifying tissue-specific variants is essential in order to perceive the selective vulnerability in AD. In current work, we aligned mRNA-seq data with HG19/HG38 genomic assembly and identified specific variations present in temporal, frontal and other lobes of the AD using sequence alignment map tools. We compared the results with the genome-wide association and gene expression quantitative trait loci studies of the various neurological disorders. We also distinguished variants and epitranscriptomic modifications through the RNA-modification database and evaluated the variant effect in the coding/UTR regions. In addition, we developed genetic and functional interaction networks to understand the relationship between predicted vulnerable variations and differentially expressed genes. We found that genes involved in gliogenesis, intermediate filament organization are altered in the temporal lobe. Oxidative phosphorylation, and calcium ion homeostasis are modified in the frontal lobe, and protein degradation, apoptotic signaling are altered in other lobes. From this study, we propose that disruption of glial cell structural integrity, defective gliogenesis, and failure in glia-neuron communication are the primary factors for selective vulnerability.

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  • Tensor decomposition-based unsupervised feature extraction identifies candidate genes that induce post-traumatic stress disorder-mediated heart diseases Reviewed International journal

    Y. -H. Taguchi

    BMC MEDICAL GENOMICS   10 ( S4 )   p.67   2017.12

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    Background: Although post-traumatic stress disorder (PTSD) is primarily a mental disorder, it can cause additional symptoms that do not seem to be directly related to the central nervous system, which PTSD is assumed to directly affect. PTSD-mediated heart diseases are some of such secondary disorders. In spite of the significant correlations between PTSD and heart diseases, spatial separation between the heart and brain (where PTSD is primarily active) prevents researchers from elucidating the mechanisms that bridge the two disorders. Our purpose was to identify genes linking PTSD and heart diseases.
    Methods: In this study, gene expression profiles of various murine tissues observed under various types of stress or without stress were analyzed in an integrated manner using tensor decomposition (TD).
    Results: Based upon the obtained features, similar to 400 genes were identified as candidate genes that may mediate heart diseases associated with PTSD. Various gene enrichment analyses supported biological reliability of the identified genes. Ten genes encoding protein-, DNA-, or mRNA-interacting proteins-ILF2, ILF3, ESR1, ESR2, RAD21, HTT, ATF2, NR3C1, TP53, and TP63-were found to be likely to regulate expression of most of these similar to 400 genes and therefore are candidate primary genes that cause PTSD-mediated heart diseases. Approximately 400 genes in the heart were also found to be strongly affected by various drugs whose known adverse effects are related to heart diseases and/or fear memory conditioning; these data support the reliability of our findings.
    Conclusions: TD-based unsupervised feature extraction turned out to be a useful method for gene selection and successfully identified possible genes causing PTSD-mediated heart diseases.

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  • Genetic Association between Amyotrophic Lateral Sclerosis and Cancer Reviewed International coauthorship International journal

    Y-h. Taguchi, Hsiuying Wang

    GENES   8 ( 10 )   E243   2017.10

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    Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. An ALS drug, Riluzole, has been shown to induce two different anticancer effects on hepatocellular carcinoma (HCC). In light of this finding, we explore the relationship between ALS and cancer, especially for HCC, from the molecular biological viewpoint. We establish biomarkers that can discriminate between ALS patients and healthy controls. A principal component analysis (PCA) based unsupervised feature extraction (FE) is used to find gene biomarkers of ALS based on microarray gene expression data. Based on this method, 101 probes were selected as biomarkers for ALS with 95% high accuracy to discriminate between ALS patients and controls. Most of the genes corresponding to these probes are shown to be related to various cancers. These findings might provide a new insight for developing new therapeutic options or drugs for both ALS and cancer.

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  • Identification of candidate drugs using tensor-decomposition-based unsupervised feature extraction in integrated analysis of gene expression between diseases and DrugMatrix datasets Reviewed International journal

    Y. -H. Taguchi

    SCIENTIFIC REPORTS   7   13733   2017.10

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    Identifying drug target genes in gene expression profiles is not straightforward. Because a drug targets proteins and not mRNAs, the mRNA expression of drug target genes is not always altered. In addition, the interaction between a drug and protein can be context dependent; this means that simple drug incubation experiments on cell lines do not always reflect the real situation during active disease. In this paper, I applied tensor-decomposition-based unsupervised feature extraction to the integrated analysis using a mathematical product of gene expression in various diseases and gene expression in the DrugMatrix dataset, where comprehensive data on gene expression during various drug treatments of rats are reported. I found that this strategy, in a fully unsupervised manner, enables researchers to identify a combined set of genes and compounds that significantly overlap with gene and drug interactions identified in the past. As an example illustrating the usefulness of this strategy in drug discovery experiments, I considered cirrhosis, for which no effective drugs have ever been proposed. The present strategy identified two promising therapeutic-target genes, CYPOR and HNFA4; for their protein products, bezafibrate was identified as a promising candidate drug, supported by in silico docking analysis.

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  • One-class Differential Expression Analysis using Tensor Decomposition-based Unsupervised Feature Extraction Applied to Integrated Analysis of Multiple Omics Data from 26 Lung Adenocarcinoma Cell Lines Reviewed International coauthorship International journal

    Y. h. Taguchi

    2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)   131 - 138   2017.10

  • An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes Reviewed International coauthorship International journal

    Shuntaro Chiba, Takashi Ishida, Kazuyoshi Ikeda, Masahiro Mochizuki, Reiji Teramoto, Y-h. Taguchi, Mitsuo Iwadate, Hideaki Umeyama, Chandrasekaran Ramakrishnan, A. Mary Thangakani, D. Velmurugan, M. Michael Gromiha, Tatsuya Okuno, Koya Kato, Shintaro Minami, George Chikenji, Shogo D. Suzuki, Keisuke Yanagisawa, Woong-Hee Shin, Daisuke Kihara, Kazuki Z. Yamamoto, Yoshitaka Moriwaki, Nobuaki Yasuo, Ryunosuke Yoshino, Sergey Zozulya, Petro Borysko, Roman Stavniichuk, Teruki Honma, Takatsugu Hirokawa, Yutaka Akiyama, Masakazu Sekijima

    SCIENTIFIC REPORTS   7   12038   2017.9

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    We propose a new iterative screening contest method to identify target protein inhibitors. After conducting a compound screening contest in 2014, we report results acquired from a contest held in 2015 in this study. Our aims were to identify target enzyme inhibitors and to benchmark a variety of computer-aided drug discovery methods under identical experimental conditions. In both contests, we employed the tyrosine-protein kinase Yes as an example target protein. Participating groups virtually screened possible inhibitors from a library containing 2.4 million compounds. Compounds were ranked based on functional scores obtained using their respective methods, and the top 181 compounds from each group were selected. Our results from the 2015 contest show an improved hit rate when compared to results from the 2014 contest. In addition, we have successfully identified a statistically-warranted method for identifying target inhibitors. Quantitative analysis of the most successful method gave additional insights into important characteristics of the method used.

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  • Tensor decomposition- based unsupervised feature extraction applied to matrix products for multi-view data processing Reviewed International journal

    Y-h. Taguchi

    PLOS ONE   12 ( 8 )   e0183933   2017.8

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    In the current era of big data, the amount of data available is continuously increasing. Both the number and types of samples, or features, are on the rise. The mixing of distinct features often makes interpretation more difficult. However, separate analysis of individual types requires subsequent integration. A tensor is a useful framework to deal with distinct types of features in an integrated manner without mixing them. On the other hand, tensor data is not easy to obtain since it requires the measurements of huge numbers of combinations of distinct features; if there are m kinds of features, each of which has N dimensions, the number of measurements needed are as many as N m, which is often too large to measure. In this paper, I propose a new method where a tensor is generated from individual features without combinatorial measurements, and the generated tensor was decomposed back to matrices, by which unsupervised feature extraction was performed. In order to demonstrate the usefulness of the proposed strategy, it was applied to synthetic data, as well as three omics datasets. It outperformed other matrix-based methodologies.

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  • Expression of Serum Exosomal and Esophageal MicroRNA in Rat Reflux Esophagitis Reviewed International journal

    Risa Uemura, Yoshiki Murakami, Atsushi Hashimoto, Akinari Sawada, Koji Otani, Koichi Taira, Shuhei Hosomi, Yasuaki Nagami, Fumio Tanaka, Noriko Kamata, Hirokazu Yamagami, Tetsuya Tanigawa, Toshio Watanabe, Y-h Taguchi, Yasuhiro Fujiwara

    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES   18 ( 8 )   1611   2017.8

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    Gastroesophageal reflux disease (GERD) is a common upper gastrointestinal disease. However, the role of exosomal microRNAs (miRNAs) and esophageal miRNAs in GERD has not been studied. A rat model of acid reflux esophagitis was used to establish a novel diagnosis marker for GERD and examine dynamics of miRNA expression in GERD. Rats were sacrificed 3 (acute phase), 7 (sub-acute phase) and 21 days (chronic phase) after induction of esophagitis. Exosomes were extracted from serum, and the expression patterns of serum miRNAs were analyzed. Four upregulated miRNAs (miR-29a-3p, 128-3p, 223-3p and 3473) were identified by microarray analysis. The expression levels of exosomal miR-29a-3p were significantly higher in the chronic phase of reflux esophagitis compared with controls, and increased expression of miR-29a-3p was specific to chronic reflux esophagitis. Esophageal miR-223-3p expression was higher compared with controls, and gradually decreased from acute to chronic phase in esophagitis. In conclusion, exosomal miR-29a-3p and esophageal miR-223-3p might play roles in GERD.

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  • Principal component analysis based unsupervised feature extraction applied to bioinformatics analysis Reviewed

    Y-h. Taguchi, Mitsuo Iwadate, Hideaki Umeyama, Yoshiki Murakami

    Computational Methods with Applications in Bioinformatics Analysis   153 - 182   2017.6

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  • Principal Components Analysis Based Unsupervised Feature Extraction Applied to Gene Expression Analysis of Blood from Dengue Haemorrhagic Fever Patients Reviewed International journal

    Y-h. Taguchi

    SCIENTIFIC REPORTS   7   44016   2017.3

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    Dengue haemorrhagic fever (DHF) sometimes occurs after recovery from the disease caused by Dengue virus (DENV), and is often fatal. However, the mechanism of DHF has not been determined, possibly because no suitable methodologies are available to analyse this disease. Therefore, more innovative methods are required to analyse the gene expression profiles of DENV-infected patients. Principal components analysis (PCA)-based unsupervised feature extraction (FE) was applied to the gene expression profiles of DENV-infected patients, and an integrated analysis of two independent data sets identified 46 genes as critical for DHF progression. PCA using only these 46 genes rendered the two data sets highly consistent. The application of PCA to the 46 genes of an independent third data set successfully predicted the progression of DHF. A fourth in vitro data set confirmed the identification of the 46 genes. These 46 genes included interferon-and heme-biosynthesis-related genes. The former are enriched in binding sites for STAT1, STAT2, and IRF1, which are associated with DHF-promoting antibody-dependent enhancement, whereas the latter are considered to be related to the dysfunction of spliceosomes, which may mediate haemorrhage. These results are outcomes that other type of bioinformatic analysis could hardly achieve.

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  • Microarray analysis of circulating microRNAs in familial Mediterranean fever Reviewed International journal

    Taizo Wada, Tomoko Toma, Yusuke Matsuda, Akihiro Yachie, Saori Itami, Y-h Taguchi, Yoshiki Murakami

    MODERN RHEUMATOLOGY   27 ( 6 )   1040 - 1046   2017

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    Objectives: Familial Mediterranean fever (FMF) is an autoinflammatory disease caused by mutations in MEFV. Mutations in exon 10 are associated with typical FMF phenotypes, whereas the pathogenic role of variants in exons 2 and 3 remains uncertain. Recent evidence suggests that circulating microRNAs (miRNAs) are potentially useful biomarkers in several diseases. Therefore, their expression was assessed in FMF.Methods: The subjects were 24 patients with FMF who were between attacks: eight with exon 10 mutations (group A), eight with exon 3 mutations (group B), and eight without exon 3 or 10 mutations (group C). We also investigated eight cases of PFAPA as disease controls. Exosome-rich fractionated RNA was subjected to miRNA profiling by microarray.Results: Using the expression patterns of 26 miRNAs, we classified FMF (groups A, B, and C) and PFAPA with 78.1% accuracy. In FMF patients, groups A and B, A and C, and B and C were distinguished with 93.8, 87.5, and 100% accuracy using 24, 30, and 25 miRNA expression patterns, respectively.Conclusions: These findings suggest that expression patterns of circulating miRNAs differ among FMF subgroups based on MEFV mutations between FMF episodes. These patterns may serve as a useful biomarker for detecting subgroups of FMF.

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  • Identification of candidate drugs for heart failure using tensor decomposition-based unsupervised feature extraction applied to integrated analysis of gene expression between heart failure and drugmatrix datasets Reviewed

    Y. H. Taguchi

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   10362   517 - 528   2017

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    Identifying drug target genes in gene expression profiles is not straightforward. Because a drug targets not mRNAs but proteins, mRNA expression of drug target genes is not always altered. In addition, the interaction between a drug and protein can be context dependent
    this means that simple drug incubation experiments on cell lines do not always reflect the real situation during active disease. In this paper, I apply tensor decomposition-based unsupervised feature extraction to the integrated analysis of gene expression between heart failure and the DrugMatrix dataset where comprehensive data on gene expression during various drug treatments of rats were reported. I found that this strategy, in a fully unsupervised manner, enables us to identify a combined set of genes and compounds, for which various associations with heart failure were reported.

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  • Principal component analysis based unsupervised feature extraction applied to publicly available gene expression profiles provides new insights into the mechanisms of action of histone deacetylase inhibitors Reviewed International journal

    Y. H. Taguchi

    Neuroepigenetics   8   1 - 18   2016.12

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    Publicly available gene expression profiles of the hippocampus measured during the successful administration of the histone deacetylase inhibitor, CI-994, to assist the extinction of mice contextual fear conditioning were re-analyzed using the recently proposed principal component analysis based unsupervised feature extraction. We identified 30 genes associated with differential gene expression in the hippocampus of mice treated with the HDAC inhibitor compared to controls
    most of these genes code for postsynaptic density proteins. These 30 genes significantly overlapped with those detected by treatment with another HDAC inhibitor, FTY720, during similar contextual fear conditioning. However, because the 30 genes did not strongly overlap with genes associated with histone acetylation during contextual fear conditioning, altered histone modification in response to HDAC inhibitor treatment might not be the primary mechanism of effective extinction of contextual fear conditioning. Based on the results of our analyses we propose that HDAC inhibitors affect the temporal expression of the above genes via direct as well as indirect mechanisms that involve calcium signaling.

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  • Identification of exosomal miRNAs in early stage of hepatocarcinogenesis Reviewed

    Yoshiki Murakami, Koichi Tsuneyama, Saori Itami, Michiyo Hayakawa, Mari Ando, Toshihito Tanahashi, Mitsuoki Kawano, Norifumi Kawada, Y-h Taguchi

    HEPATOLOGY   64 ( 1 supp )   236A - 236A   2016.10

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  • SFRP1 is a possible candidate for epigenetic therapy in non-small cell lung cancer Reviewed International journal

    Y-h Taguchi, Mitsuo Iwadate, Hideaki Umeyama

    BMC MEDICAL GENOMICS   9 ( Suppl 1 )   p.28   2016.8

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    Background: Non-small cell lung cancer (NSCLC) remains a lethal disease despite many proposed treatments. Recent studies have indicated that epigenetic therapy, which targets epigenetic effects, might be a new therapeutic methodology for NSCLC. However, it is not clear which objects (e.g., genes) this treatment specifically targets. Secreted frizzled-related proteins (SFRPs) are promising candidates for epigenetic therapy in many cancers, but there have been no reports of SFRPs targeted by epigenetic therapy for NSCLC.
    Methods: This study performed a meta-analysis of reprogrammed NSCLC cell lines instead of the direct examination of epigenetic therapy treatment to identify epigenetic therapy targets. In addition, mRNA expression/promoter methylation profiles were processed by recently proposed principal component analysis based unsupervised feature extraction and categorical regression analysis based feature extraction.
    Results: The Wnt/beta-catenin signalling pathway was extensively enriched among 32 genes identified by feature extraction. Among the genes identified, SFRP1 was specifically indicated to target beta-catenin, and thus might be targeted by epigenetic therapy in NSCLC cell lines. A histone deacetylase inhibitor might reactivate SFRP1 based upon the re-analysis of a public domain data set. Numerical computation validated the binding of SFRP1 to WNT1 to suppress Wnt signalling pathway activation in NSCLC.
    Conclusions: The meta-analysis of reprogrammed NSCLC cell lines identified SFRP1 as a promising target of epigenetic therapy for NSCLC.

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  • Comparative Gene Expression Analysis of Mouse and Human Cardiac Maturation Reviewed International coauthorship International journal

    Hideki Uosaki, Y-h Taguchi

    GENOMICS PROTEOMICS & BIOINFORMATICS   14 ( 4 )   207 - 215   2016.8

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    Understanding how human cardiomyocytes mature is crucial to realizing stem cell-based heart regeneration, modeling adult heart diseases, and facilitating drug discovery. However, it is not feasible to analyze human samples for maturation due to inaccessibility to samples while cardiomyocytes mature during fetal development and childhood, as well as difficulty in avoiding variations among individuals. Using model animals such as mice can be a useful strategy; nonetheless, it is not well-understood whether and to what degree gene expression profiles during maturation are shared between humans and mice. Therefore, we performed a comparative gene expression analysis of mice and human samples. First, we examined two distinct mice microarray platforms for shared gene expression profiles, aiming to increase reliability of the analysis. We identified a set of genes displaying progressive changes during maturation based on principal component analysis. Second, we demonstrated that the genes identified had a differential expression pattern between adult and earlier stages (e.g., fetus) common in mice and humans. Our findings provide a foundation for further genetic studies of cardiomyocyte maturation.

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  • Inference of non-small cell lung cancer causing genes via integrated analysis of multi-omics data set Reviewed

    Y-h. Taguchi

    Medical Science Digest   394 - 397   2016.7

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  • Principal component analysis based unsupervised feature extraction applied to budding yeast temporally periodic gene expression Reviewed International journal

    Y-h Taguchi

    BIODATA MINING   9 ( 1 )   p.22   2016.6

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    Background: The recently proposed principal component analysis (PCA) based unsupervised feature extraction (FE) has successfully been applied to various bioinformatics problems ranging from biomarker identification to the screening of disease causing genes using gene expression/epigenetic profiles. However, the conditions required for its successful use and the mechanisms involved in how it outperforms other supervised methods is unknown, because PCA based unsupervised FE has only been applied to challenging (i.e. not well known) problems.
    Results: In this study, PCA based unsupervised FE was applied to an extensively studied organism, i.e., budding yeast. When applied to two gene expression profiles expected to be temporally periodic, yeast metabolic cycle (YMC) and yeast cell division cycle (YCDC), PCA based unsupervised FE outperformed simple but powerful conventional methods, with sinusoidal fitting with regards to several aspects: (i) feasible biological term enrichment without assuming periodicity for YMC; (ii) identification of periodic profiles whose period was half as long as the cell division cycle for YMC; and (iii) the identification of no more than 37 genes associated with the enrichment of biological terms related to cell division cycle for the integrated analysis of seven YCDC profiles, for which sinusoidal fittings failed. The explantation for differences between methods used and the necessary conditions required were determined by comparing PCA based unsupervised FE with fittings to various periodic (artificial, thus pre-defined) profiles. Furthermore, four popular unsupervised clustering algorithms applied to YMC were not as successful as PCA based unsupervised FE.
    Conclusions: PCA based unsupervised FE is a useful and effective unsupervised method to investigate YMC and YCDC. This study identified why the unsupervised method without pre-judged criteria outperformed supervised methods requiring human defined criteria.

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  • Identification of More Feasible MicroRNA-mRNA Interactions within Multiple Cancers Using Principal Component Analysis Based Unsupervised Feature Extraction Reviewed International journal

    Y-h. Taguchi

    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES   17 ( 5 )   p.E696   2016.5

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    MicroRNA(miRNA)-mRNA interactions are important for understanding many biological processes, including development, differentiation and disease progression, but their identification is highly context-dependent. When computationally derived from sequence information alone, the identification should be verified by integrated analyses of mRNA and miRNA expression. The drawback of this strategy is the vast number of identified interactions, which prevents an experimental or detailed investigation of each pair. In this paper, we overcome this difficulty by the recently proposed principal component analysis (PCA)-based unsupervised feature extraction (FE), which reduces the number of identified miRNA-mRNA interactions that properly discriminate between patients and healthy controls without losing biological feasibility. The approach is applied to six cancers: hepatocellular carcinoma, non-small cell lung cancer, esophageal squamous cell carcinoma, prostate cancer, colorectal/colon cancer and breast cancer. In PCA-based unsupervised FE, the significance does not depend on the number of samples (as in the standard case) but on the number of features, which approximates the number of miRNAs/mRNAs. To our knowledge, we have newly identified miRNA-mRNA interactions in multiple cancers based on a single common (universal) criterion. Moreover, the number of identified interactions was sufficiently small to be sequentially curated by literature searches.

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  • MicroRNA expression in hepatocellular carcinoma after the eradication of chronic hepatitis virus C infection using interferon therapy Reviewed

    Akihiro Tamori, Yoshiki Murakami, Shoji Kubo, Saori Itami, Sawako Uchida-Kobayashi, Hiroyasu Morikawa, Masaru Enomoto, Shigekazu Takemura, Toshihito Tanahashi, Y-h. Taguchi, Norifumi Kawada

    HEPATOLOGY RESEARCH   46 ( 3 )   E26 - E35   2016.3

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    AimHepatocellular carcinoma (HCC) develops in up to 5% of patients after the successful treatment of chronic hepatitis C virus (HCV) infection using interferon therapy. The aim of this study was to characterize miRNA expression in liver tissues from patients who achieved a sustained viral response (SVR).
    MethodsSeventy-one patients with resected HCC were enrolled into the present study: 61 HCC from patients with continuously infected HCV (HCV-HCC) and 10 from patients who had achieved SVR (SVR-HCC). We also included non-tumor tissues (SVR-NT) from four patients with SVR-HCC, and liver tissue (SVR-CH) from four SVR patients without HCC. Total RNA was extracted from liver samples. The miRNA expression patterns were analyzed using microarrays. In addition, target gene expression was quantified after miRNA overexpression in HEK293 cells.
    ResultsWe could discriminate between SVR-HCC and HCV-HCC with 75.36% accuracy using the expression pattern of six specific miRNA. The expression levels of 37 miRNA were significantly lower in HCV-HCC than in SVR-HCC, whereas the expression of 25 miRNA was significantly higher in HCV-HCC than SVR-HCC (P&lt;1.0E-05). The expression of thrombospondin 1 was regulated in an opposing manner by miR-30a-3p in SVR-HCC and HCV-HCC. In non-tumor tissues, the expression pattern of seven miRNA could distinguish between SVR-CH and SVR-NT with 87.50% accuracy.
    ConclusionComprehensive miRNA expression analyses could not only differentiate between SVR-HCC and HCV-HCC but also forecast hepatocarcinogenesis after achieving SVR.

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  • microRNA-mRNA interaction identification in Wilms tumor using principal component analysis based unsupervised feature extraction Reviewed International coauthorship International journal

    Y-h. Taguchi

    2016 IEEE 16TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE)   71 - 78   2016

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    Wilms tumor is one of lethal child renal cancers, for which no known disease causing mechanisms exist. In this paper, we tried to identify possible disease causing microRNA(miRNA)-mRNA pairs (interactions) by analyzing (partially matched) miRNA/mRNA gene expression profiles with the recently proposed principal component analysis based unsupervised feature extraction. It successfully identified multiple miRNA-mRNA pairs whose biological natures are convincing. Correlation coefficients between miRNA and mRNA expression in matched parts of profiles turned out to be significantly negative. Constructed miRNA-mRNA network will be a key to understand Wilms tumor causing mechanisms.

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  • Identification of aberrant gene expression associated with aberrant promoter methylation in primordial germ cells between E13 and E16 rat F3 generation vinclozolin lineage Reviewed International journal

    Y-h Taguchi

    BMC BIOINFORMATICS   16 ( Supp 18 )   S16   2015.12

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    Background: Transgenerational epigenetics (TGE) are currently considered important in disease, but the mechanisms involved are not yet fully understood. TGE abnormalities expected to cause disease are likely to be initiated during development and to be mediated by aberrant gene expression associated with aberrant promoter methylation that is heritable between generations. However, because methylation is removed and then reestablished during development, it is not easy to identify promoter methylation abnormalities by comparing normal lineages with those expected to exhibit TGE abnormalities.
    Methods: This study applied the recently proposed principal component analysis (PCA)-based unsupervised feature extraction to previously reported and publically available gene expression/promoter methylation profiles of rat primordial germ cells, between E13 and E16 of the F3 generation vinclozolin lineage that are expected to exhibit TGE abnormalities, to identify multiple genes that exhibited aberrant gene expression/promoter methylation during development.
    Results: The biological feasibility of the identified genes were tested via enrichment analyses of various biological concepts including pathway analysis, gene ontology terms and protein-protein interactions. All validations suggested superiority of the proposed method over three conventional and popular supervised methods that employed t test, limma and significance analysis of microarrays, respectively. The identified genes were globally related to tumors, the prostate, kidney, testis and the immune system and were previously reported to be related to various diseases caused by TGE.
    Conclusions: Among the genes reported by PCA-based unsupervised feature extraction, we propose that chemokine signaling pathways and leucine rich repeat proteins are key factors that initiate transgenerational epigenetic-mediated diseases, because multiple genes included in these two categories were identified in this study.

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  • Comprehensive analysis of transcriptome and metabolome analysis in Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma Reviewed International journal

    Yoshiki Murakami, Shoji Kubo, Akihiro Tamori, Saori Itami, Etsushi Kawamura, Keiko Iwaisako, Kazuo Ikeda, Norifumi Kawada, Takahiro Ochiya, Y-H Taguchi

    SCIENTIFIC REPORTS   5   p.16294   2015.11

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    Intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) are liver originated malignant tumors. Of the two, ICC has the worse prognosis because it has no reliable diagnostic markers and its carcinogenic mechanism is not fully understood. The aim of this study was to integrate metabolomics and transcriptomics datasets to identify variances if any in the carcinogenic mechanism of ICC and HCC. Ten ICC and 6 HCC who were resected surgically, were enrolled. miRNA and mRNA expression analysis were performed by microarray on ICC and HCC and their corresponding non-tumor tissues (ICC_NT and HCC_NT). Compound analysis was performed using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS). Principle component analysis (PCA) revealed that among the four sample groups (ICC, ICC_NT, HCC, and HCC_NT) there were 14 compounds, 62 mRNAs and 17 miRNAs with two distinct patterns: tumor and non-tumor, and ICC and non-ICC. We accurately (84.38%) distinguished ICC by the distinct pattern of its compounds. Pathway analysis using transcriptome and metabolome showed that several pathways varied between tumor and non-tumor samples. Based on the results of the PCA, we believe that ICC and HCC have different carcinogenic mechanism therefore knowing the specific profile of genes and compounds can be useful in diagnosing ICC.

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  • Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target Reviewed International coauthorship International journal

    Shuntaro Chiba, Kazuyoshi Ikeda, Takashi Ishida, M. Michael Gromiha, Y-h. Taguchi, Mitsuo Iwadate, Hideaki Umeyama, Kun-Yi Hsin, Hiroaki Kitano, Kazuki Yamamoto, Nobuyoshi Sugaya, Koya Kato, Tatsuya Okuno, George Chikenji, Masahiro Mochizuki, Nobuaki Yasuo, Ryunosuke Yoshino, Keisuke Yanagisawa, Tomohiro Ban, Reiji Teramoto, Chandrasekaran Ramakrishnan, A. Mary Thangakani, D. Velmurugan, Philip Prathipati, Junichi Ito, Yuko Tsuchiya, Kenji Mizuguchi, Teruki Honma, Takatsugu Hirokawa, Yutaka Akiyama, Masakazu Sekijima

    SCIENTIFIC REPORTS   5   2015.11

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    A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.

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  • MicroRNA expression in hepatocellular carcinoma and non-cancerous livers after the eradication of hepatitis virus C using interferon-based therapy Reviewed

    Akihiro Tamori, Yoshiki Murakami, Shoji Kubo, Saori Itami, Sawako K. Uchida, Hiroyasu Morikawa, Masaru Enomoto, Shigekazu Takemura, Toshihito Tanahashi, Y-h Taguchi, Norifumi Kawada

    HEPATOLOGY   62   425A - 426A   2015.10

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  • Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease Reviewed International journal

    Y-h Taguchi, Mitsuo Iwadate, Hideaki Umeyama

    BMC BIOINFORMATICS   16   139   2015.4

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    Background: Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems.
    Results: Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery was performed as translational validation of the methods.
    Conclusions: Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug discovery.

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  • Exploring effective multiplicity in multichannel functional near-infrared spectroscopy using eigenvalues of correlation matrices Reviewed International journal

    Minako Uga, Ippeita Dan, Haruka Dan, Yasushi Kyutoku, Y-h Taguchi, Eiju Watanabe

    NEUROPHOTONICS   2 ( 1 )   p.015002   2015.1

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    Recent advances in multichannel functional near-infrared spectroscopy (fNIRS) allow wide coverage of cortical areas while entailing the necessity to control family-wise errors (FWEs) due to increased multiplicity. Conventionally, the Bonferroni method has been used to control FWE. While Type I errors (false positives) can be strictly controlled, the application of a large number of channel settings may inflate the chance of Type II errors (false negatives). The Bonferroni-based methods are especially stringent in controlling Type I errors of the most activated channel with the smallest p value. To maintain a balance between Types I and II errors, effective multiplicity (M-eff) derived from the eigenvalues of correlation matrices is a method that has been introduced in genetic studies. Thus, we explored its feasibility in multichannel fNIRS studies. Applying the M-eff method to three kinds of experimental data with different activation profiles, we performed resampling simulations and found that M-eff was controlled at 10 to 15 in a 44-channel setting. Consequently, the number of significantly activated channels remained almost constant regardless of the number of measured channels. We demonstrated that the M-eff approach can be an effective alternative to Bonferroni-based methods for multichannel fNIRS studies. (C) The Authors.

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  • Heuristic principal component analysis-based unsupervised feature extraction and its application to gene expression analysis of amyotrophic lateral sclerosis data sets Reviewed

    Y-h. Taguchi, Mitsuo Iwadate, Hideaki Umeyama

    2015 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (CIBCB)   8 - 17   2015

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    We applied principal component analysis (PCA)based unsupervised feature extraction (FE) to amyotrophic lateral sclerosis (ALS) gene expression profiles. ALS is a debilitating neurodegenerative disorder with no effective therapy. The relevant gene expression profiles contained a small number of samples (from a few to tens) with a large number of features (several tens of thousands). Although it is important to recognize critical genes from gene expression profiles, a small-sample-large-feature situation makes FE difficult. In PCA-based unsupervised FE, features rather than samples are embedded into a low dimensional space, and critical genes are identified as outliers that are supposed to obey group-oriented behavior. The 29 candidate genes identified as critical for ALS by this methodology turned out to be biologically feasible based on comparisons with numerous previous studies. Together, they formed a collected gene regulation/protein binding network within which the known, but not explicitly identified in this study, three ALS-causing genes, SOD1, TDP-43, and SETX, could be naturally placed/embedded. Among the 29 genes, the translated chemokine receptor CCR6 protein was considered to be a potential therapy target and its antagonists/agonists were identified using the in silico drug discovery software ChooseLD. The ten top-ranked antagonists/agonists shared structures with many compounds that were previously known to bind to various proteins.

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  • In silico spleen tyrosine kinase inhibitor screening by chooseLD Reviewed

    Hideaki Umeyama, Mitsuo Iwadate, Y. H. Taguchi

    IPSJ Transactions on Bioinformatics   8   14 - 20   2015

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    Background: Spleen tyrosine kinase (SYK) is a protein related to various diseases. Aberrant SYK expression often causes the progression and initiation of several diseases including cancer and autoimmune diseases. Despite the importance of inhibiting SYK and identifying candidate inhibitors, no clinically effective inhibitors have been reported to date. Therefore, there is a need for novel SYK inhibitors. Results: Candidate compounds were investigated using in silico screening by chooseLD, which simulates ligand docking to proteins. Using this system, known inhibitors were correctly recognized as compounds with high affinity to SYK. Furthermore, many compounds in the DrugBank database were newly identified as having high affinity to the ATP-binding sites in the kinase domain with a similar affinity to previously reported inhibitors. Conclusions: Many drug candidate compounds from the DrugBank database were newly identified as inhibitors of SYK. Because compounds registered in the DrugBank are expected to have fewer side effects than currently available compounds, these newly identified compounds may be clinically useful inhibitors of SYK for the treatment of various diseases.

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  • Apparent microRNA-Target-specific Histone Modification in Mammalian Spermatogenesis Reviewed International journal

    Y-H. Taguchi

    EVOLUTIONARY BIOINFORMATICS   11   13 - 26   2015

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    BACKGROUND: Epigenetics is an important mRNA expression regulator. However, how distinct epigenetic factors, such as microRNAs (miRNAs) and promoter methylation, cooperatively regulate mRNA expression is rarely discussed. Recently, apparent miRNA regulation of promoter methylation was identified by bioinformatic analysis; however, it has not yet been experimentally confirmed. If miRNA regulation of other epigenetic factors were identified, it would reveal another layer of epigenetic regulation. In this paper, histone modifications (H3K4me1, H3K4me3, H3K27me3, H3K27ac, H3K9ac, and H2AZ) during mammalian spermatogenesis were studied and the apparent miRNA-target-specific histone modification was investigated by bioinformatic analyses of publicly available datasets.
    RESULTS: We identified several miRNAs' target genes that are significantly associated with histone modification during mammalian spermatogenesis. MiRNAs that target genes associated with the most significant histone modifications are expressed before or during spermatogenesis; thus the results were convincing.
    CONCLUSIONS: In this paper, we identified apparent miRNA regulation of histone modifications using a bioinformatics approach. The biological mechanisms of this effect should be further experimentally investigated.

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  • TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasis in non-small cell lung cancer Reviewed International journal

    Hideaki Umeyama, Mitsuo Iwadate, Y-h Taguchi

    BMC GENOMICS   15 ( Suppl 9 )   S2   2014.12

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    Background: Non-small cell lung cancer (NSCLC) remains lethal despite the development of numerous drug therapy technologies. About 85% to 90% of lung cancers are NSCLC and the 5-year survival rate is at best still below 50%. Thus, it is important to find drugable target genes for NSCLC to develop an effective therapy for NSCLC.
    Results: Integrated analysis of publically available gene expression and promoter methylation patterns of two highly aggressive NSCLC cell lines generated by in vivo selection was performed. We selected eleven critical genes that may mediate metastasis using recently proposed principal component analysis based unsupervised feature extraction. The eleven selected genes were significantly related to cancer diagnosis. The tertiary protein structure of the selected genes was inferred by Full Automatic Modeling System, a profile-based protein structure inference software, to determine protein functions and to specify genes that could be potential drug targets.
    Conclusions: We identified eleven potentially critical genes that may mediate NSCLC metastasis using bioinformatic analysis of publically available data sets. These genes are potential target genes for the therapy of NSCLC. Among the eleven genes, TINAGL1 and B3GALNT1 are possible candidates for drug compounds that inhibit their gene expression.

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  • Heuristic principal component analysis-based unsupervised feature extraction and its application to bioinformatics Reviewed

    Y. H. Taguchi, Mitsuo Iwadate, Hideaki Umeyama, Yoshiki Murakami, Akira Okamoto

    Big Data Analytics in Bioinformatics and Healthcare   138 - 162   2014.10

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    Feature Extraction (FE) is a difficult task when the number of features is much larger than the number of samples, although that is a typical situation when biological (big) data is analyzed. This is especially true when FE is stable, independent of the samples considered (stable FE), and is often required. However, the stability of FE has not been considered seriously. In this chapter, the authors demonstrate that Principal Component Analysis (PCA)-based unsupervised FE functions as stable FE. Three bioinformatics applications of PCA-based unsupervised FE-detection of aberrant DNA methylation associated with diseases, biomarker identification using circulating microRNA, and proteomic analysis of bacterial culturing processes-are discussed.

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  • Comparison of Hepatocellular Carcinoma miRNA Expression Profiling as Evaluated by Next Generation Sequencing and Microarray Reviewed International journal

    Yoshiki Murakami, Toshihito Tanahashi, Rina Okada, Hidenori Toyoda, Takashi Kumada, Masaru Enomoto, Akihiro Tamori, Norifumi Kawada, Y-h Taguchi, Takeshi Azuma

    PLOS ONE   9 ( 9 )   e106314   2014.9

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    MicroRNA (miRNA) expression profiling has proven useful in diagnosing and understanding the development and progression of several diseases. Microarray is the standard method for analyzing miRNA expression profiles; however, it has several disadvantages, including its limited detection of miRNAs. In recent years, advances in genome sequencing have led to the development of next-generation sequencing (NGS) technologies, which significantly advance genome sequencing speed and discovery. In this study, we compared the expression profiles obtained by next generation sequencing (NGS) with the profiles created using microarray to assess if NGS could produce a more accurate and complete miRNA profile. Total RNA from 14 hepatocellular carcinoma tumors (HCC) and 6 matched non-tumor control tissues were sequenced with Illumina MiSeq 50-bp single-end reads. Micro RNA expression profiles were estimated using miRDeep2 software. As a comparison, miRNA expression profiles for 11 out of 14 HCCs were also established by microarray (Agilent human microRNA microarray). The average total sequencing exceeded 2.2 million reads per sample and of those reads, approximately 57% mapped to the human genome. The average correlation for miRNA expression between microarray and NGS and subtraction were 0.613 and 0.587, respectively, while miRNA expression between technical replicates was 0.976. The diagnostic accuracy of HCC, p-value, and AUC were 90.0%, 7.22x10(-4), and 0.92, respectively. In summary, NGS created an miRNA expression profile that was reproducible and comparable to that produced by microarray. Moreover, NGS discovered novel miRNAs that were otherwise undetectable by microarray. We believe that miRNA expression profiling by NGS can be a useful diagnostic tool applicable to multiple fields of medicine.

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  • Universal disease biomarker: Can a fixed set of blood microRNAs diagnose multiple diseases? Reviewed International journal

    Y. H. Taguchi, Yoshiki Murakami

    BMC Research Notes   7 ( 1 )   2014.8

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    Background: The selection of disease biomarkers is often difficult because of their unstable identification, i.e., the selection of biomarkers is heavily dependent upon the set of samples analyzed and the use of independent sets of samples often results in a completely different set of biomarkers being identified. However, if a fixed set of disease biomarkers could be identified for the diagnosis of multiple diseases, the difficulties of biomarker selection could be reduced.
    Results: In this study, the previously identified universal disease biomarker (UDB) consisting of blood miRNAs that could discriminate between patients with multiple diseases and healthy controls was extended to the recently reported independent measurements of blood microRNAs (miRNAs). The performance achieved by UDB in an independent set of samples was competitive with performances achieved with biomarkers selected using lasso, a standard, heavily sample-dependent procedure. Furthermore, the development of stable feature extraction was suggested to be a key factor in constructing more efficient and stable (i.e., sample-and disease-independent) UDBs.
    Conclusions: The previously proposed UDB was successfully extended to an additional seven diseases and is expected to be useful for the diagnosis of other diseases.

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  • Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as candidate drug targets Reviewed International journal

    Ryoichi Kinoshita, Mitsuo Iwadate, Hideaki Umeyama, Y-h Taguchi

    BMC SYSTEMS BIOLOGY   8 ( Suppl 1 )   S4   2014.1

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    Background: Aberrant DNA methylation is often associated with cancers. Thus, screening genes with cancer-associated aberrant DNA methylation is a useful method to identify candidate cancer-causing genes. Aberrant DNA methylation is also genotype dependent. Thus, the selection of genes with genotype-specific aberrant DNA methylation in cancers is potentially important for tailor-made medicine. The selected genes are important candidate drug targets.
    Results: The recently proposed principal component analysis based selection of genes with aberrant DNA methylation was applied to genotype and DNA methylation patterns in squamous cell carcinoma measured using single nucleotide polymorphism (SNP) arrays. SNPs that are frequently found in cancers are usually highly methylated, and the genes that were selected using this method were reported previously to be related to cancers. Thus, genes with genotype-specific DNA methylation patterns will be good therapeutic candidates. The tertiary structures of the proteins encoded by the selected genes were successfully inferred using two profile-based protein structure servers, FAMS and Phyre2. Candidate drugs for three of these proteins, tyrosine kinase receptor (ALK), EGLN3 protein, and NUAK family SNF1-like kinase 1 (NUAK1), were identified by ChooseLD.
    Conclusions: We detected genes with genotype-specific DNA methylation in squamous cell carcinoma that are candidate drug targets. Using in silico drug discovery, we successfully identified several candidate drugs for the ALK, EGLN3 and NUAK1 genes that displayed genotype-specific DNA methylation.

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  • Bacterial type 3 secretion system effector proteins are distinct between plant symbiotic, plant pathogenic and animal pathogenic bacteria Reviewed

    Yuuichi Nakano, Mitsuo Iwadate, Hideaki Umeyama, Y. H. Taguchi

    IPSJ Transactions on Bioinformatics   7   2 - 15   2014.1

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    Type 3 secretion system (T3SS) effector protein is a part of bacterial secretion systems. T3SS exists in the pathogenic and symbiotic bacteria. How the T3SS effector proteins in these two classes differ from each other should be interesting. In this paper, we successfully discriminated T3SS effector proteins between plant pathogenic, animal pathogenic and plant symbiotic bacteria based on feature vectors inferred computationally by Yahara et al. only from amino acid sequences. This suggests that these three classes of bacteria employ distinct T3SS effector proteins. We also hypothesized that the feature vector proposed by Yahara et al. represents protein structure, possibly protein folds defined in Structural Classification of Proteins (SCOP) database. © 2014 Information Processing Society of Japan.

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  • Integrative Analysis of Gene Expression and Promoter Methylation during Reprogramming of a Non-Small-Cell Lung Cancer Cell Line Using Principal Component Analysis-Based Unsupervised Feature Extraction Reviewed

    Y-h. Taguchi

    INTELLIGENT COMPUTING IN BIOINFORMATICS   8590   445 - 455   2014

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    Cancer cells are to some extent regarded as similar to undifferentiated cells, such as embryonic stem cells and induced pluripotent cells. However, cancer cells can be reprogrammed using standard reprogramming procedures. Thus, it would be interesting to observe the result of cancer cell reprogramming. In this paper, we reanalyzed publically available mRNA expression and promoter methylation profiles during reprogramming of non-small-cell lung cancer cell lines, using the recently proposed principal component analysis-based unsupervised feature extraction. Six genes, TGFBI, S100A6, CSRP1, CLDN11, PRKCDBP, and CRIP1, were commonly found (P = 0.003) in the 100 top-ranked genes with aberrant expression or aberrant promoter methylation. Because all six genes were related to cancer in the literature, they might be new therapeutic targets for treatment of non-small-cell lung cancer.

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  • Bioinformatic Screening of Autoimmune Disease Genes and Protein Structure Prediction with FAMS for Drug Discovery Reviewed International journal

    Shigeharu Ishida, Hideaki Umeyama, Mitsuo Iwadate, Y-H. Taguchi

    PROTEIN AND PEPTIDE LETTERS   21 ( 8 )   828 - 839   2014

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    Autoimmune diseases are often intractable because their causes are unknown. Identifying which genes contribute to these diseases may allow us to understand the pathogenesis, but it is difficult to determine which genes contribute to disease. Recently, epigenetic information has been considered to activate/deactivate disease-related genes. Thus, it may also be useful to study epigenetic information that differs between healthy controls and patients with autoimmune disease. Among several types of epigenetic information, promoter methylation is believed to be one of the most important factors. Here, we propose that principal component analysis is useful to identify specific gene promoters that are differently methylated between the normal healthy controls and patients with autoimmune disease. Full Automatic Modeling System (FAMS) was used to predict the three-dimensional structures of selected proteins and successfully inferred relatively confident structures. Several possibilities of the application to the drug discovery based on obtained structures are discussed.

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  • Principal Component Analysis Based Feature Extraction Approach to Identify Circulating microRNA Biomarkers Reviewed International journal

    Y-h. Taguchi, Yoshiki Murakami

    PLOS ONE   8 ( 6 )   e66714   2013.6

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    The discovery and characterization of blood-based disease biomarkers are clinically important because blood collection is easy and involves relatively little stress for the patient. However, blood generally reflects not only targeted diseases, but also the whole body status of patients. Thus, the selection of biomarkers may be difficult. In this study, we considered miRNAs as biomarker candidates for several reasons. First, since miRNAs were discovered relatively recently, they have not yet been tested extensively. Second, since the number of miRNAs is relatively limited, selection is expected to be easy. Third, since they are known to play critical roles in a wide range of biological processes, their expression may be disease specific. We applied a newly proposed method to select combinations of miRNAs that discriminate between healthy controls and each of 14 diseases that include 5 cancers. A new feature selection method is based on principal component analysis. Namely this method does not require knowledge of whether each sample was derived from a disease patient or a healthy control. Using this method, we found that hsa-miR-425, hsa-miR-15b, hsa-miR-185, hsa-miR-92a, hsa-miR-140-3p, hsa-miR-320a, hsa-miR486-5p, hsa-miR-16, hsa-miR-191, hsa-miR-106b, hsa-miR-19b, and hsa-miR-30d were potential biomarkers; combinations of 10 of these miRNAs allowed us to discriminate each disease included in this study from healthy controls. These 12 miRNAs are significantly up-or downregulated in most cancers and other diseases, albeit in a cancer- or disease-specific combinatory manner. Therefore, these 12 miRNAs were also previously reported to be cancer- and disease-related miRNAs. Many disease-specific KEGG pathways were also significantly enriched by target genes of up-/downregulated miRNAs within several combinations of 10 miRNAs among these 12 miRNAs. We also selected miRNAs that could discriminate one disease from another or from healthy controls. These miRNAs were found to be largely overlapped with miRNAs that discriminate each disease from healthy controls.

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  • MicroRNA-mediated regulation of target genes in several brain regions is correlated to both microRNA-targeting-specific promoter methylation and differential microRNA expression Reviewed International journal

    Y-h Taguchi

    BIODATA MINING   6 ( 1 )   2013.5

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    Background: Public domain databases nowadays provide multiple layers of genome-wide data e. g., promoter methylation, mRNA expression, and miRNA expression and should enable integrative modeling of the mechanisms of regulation of gene expression. However, researches along this line were not frequently executed.
    Results: Here, the public domain dataset of mRNA expression, microRNA (miRNA) expression and promoter methylation patterns in four regions, the frontal cortex, temporal cortex, pons and cerebellum, of human brain were sourced from the National Center for Biotechnology Informations gene expression omnibus, and reanalyzed computationally. A large number of miRNA-mediated regulation of target genes and miRNA-targeting-specific promoter methylation were identified in the six pairwise comparisons among the four brain regions. The miRNA-mediated regulation of target genes was found to be highly correlated with one or both of miRNA-targeting-specific promoter methylation and differential miRNA expression. Genes enriched for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were related to brain function and/or development were found among the target genes of miRNAs whose differential expression patterns were highly correlated with the miRNA-mediated regulation of their target genes.
    Conclusions: The combinatorial analysis of miRNA-mediated regulation of target genes, miRNA-targeting-specific promoter methylation and differential miRNA expression can help reveal the brain region-specific contributions of miRNAs to brain function and development.

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  • Microbiology- and Biodiversity-Based Modeling of Suppression of Cottony Leak of Scarlet Runner Bean in Soils with Diverse and Uniform Ecology Reviewed

    Kazunari Yokoyama, Y-h. Taguchi

    Journal of Agricultural Science and Applications   2 ( 1 )   35 - 44   2013.2

  • Correlation between miRNA-targeted-gene promoter methylation and miRNA regulation of target genes Reviewed International journal

    Y-h. Taguchi

    F1000Research   2 ( 21 )   21 - 21   2013.1

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  • O5-5 抗ヒスタミン受容体への薬物のドッキング構造と薬効の相関について(O5 アトピー性皮膚炎2,口演,第63回日本アレルギー学会秋季学術大会)

    梅山 秀明, 小松 克一郎, 海野 哲史, 田口 善弘, 岩舘 満雄

    アレルギー   62 ( 9 )   1320 - 1320   2013

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  • Structure Prediction with FAMS for Proteins Screened Critically to Autoimmune Diseases based upon Bioinformatics Reviewed

    Shigeharu Ishida, Hideaki Umeyama, Mitsuo Iwadate, Y-H. Taguchi

    BIOINFORMATICS 2013: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BIOINFORMATICS MODELS, METHODS AND ALGORITHMS   261 - 267   2013

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    Drug discovery for autoimmune diseases is recently recognized to be an important task. In this study, we try to perform structure prediction of proteins whose gene promoter regions were previously reported to be specifically methelysed or demethylased commonly for three autoimmune diseases, systemic lupus erythematosus, rheumatoid arthritis, and dermatomyositis. FAMS were employed for this purpose and we can predict three dimensional structure with significantly small enough P-values. Most of them are suggested to be self immunology related proteins and will be important drug target candidates. We also found some proteins which form complex with each other. The possibility of a new drug target, i.e., suppression of protein complex formation is suggested.

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  • Possible miRNA coregulation of target genes in brain regions by both differential miRNA expression and miRNA-targeting-specific promoter methylation Reviewed

    Y. H. Taguchi

    Communications in Computer and Information Science   375   225 - 230   2013

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    We computationally reanalyzed public domain data set deposited to gene expression omnibus, of mRNA expression, miRNA expression and promoter methylation pattern in four brain regions, i.e., frontal cortex, temporal cortex, pons and cerebellum. Then we found that more than hundreds of both miRNA regulation of target genes and miRNA-targeting-specific promoter methylation are significant on all six pairwise comparisons among the above mentioned four brain regions. We also showed that some of miRNA regulation of target genes is highly correlated with both or either of miRNA-targeting-specific promoter methylation and differential miRNA expression. We concluded that the combinatorial analysis of miRNA regulation of target genes, miRNA-targeting-specific promoter methylation and differential miRNA expression can figure out brain region specific contribution of miRNAsto brain functions and developments. © Springer-Verlag Berlin Heidelberg 2013.

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  • Comprehensive miRNA Expression Analysis in Peripheral Blood Can Diagnose Liver Disease Reviewed International journal

    Yoshiki Murakami, Hidenori Toyoda, Toshihito Tanahashi, Junko Tanaka, Takashi Kumada, Yusuke Yoshioka, Nobuyoshi Kosaka, Takahiro Ochiya, Y-h Taguchi

    PLOS ONE   7 ( 10 )   2012.10

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    Background: miRNAs circulating in the blood in a cell-free form have been acknowledged for their potential as readily accessible disease markers. Presently, histological examination is the golden standard for diagnosing and grading liver disease, therefore non-invasive options are desirable. Here, we investigated if miRNA expression profile in exosome rich fractionated serum could be useful for determining the disease parameters in patients with chronic hepatitis C (CHC).
    Methodology: Exosome rich fractionated RNA was extracted from the serum of 64 CHC and 24 controls with normal liver (NL). Extracted RNA was subjected to miRNA profiling by microarray and real-time qPCR analysis. The miRNA expression profiles from 4 chronic hepatitis B (CHB) and 12 non alcoholic steatohepatitis (NASH) patients were also established. The resulting miRNA expression was compared to the stage or grade of CHC determined by blood examination and histological inspection.
    Principal Findings: miRNAs implicated in chronic liver disease and inflammation showed expression profiles that differed from those in NL and varied among the types and grades of liver diseases. Using the expression patterns of nine miRNAs, we classified CHC and NL with 96.59% accuracy. Additionally, we could link miRNA expression pattern with liver fibrosis stage and grade of liver inflammation in CHC. In particular, the miRNA expression pattern for early fibrotic stage differed greatly from that observed in high inflammation grades.
    Conclusions: We demonstrated that miRNA expression pattern in exosome rich fractionated serum shows a high potential as a biomarker for diagnosing the grade and stage of liver diseases.

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  • Inference of Target Gene Regulation via miRNAs during Cell Senescence by Using the MiRaGE Server Reviewed International journal

    Y-H. Taguchi

    AGING AND DISEASE   3 ( 4 )   301 - 306   2012.8

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    miRNAs have recently been shown to play a key role in cell senescence, by downregulating target genes. Thus, inference of those miRNAs that critically downregulate target genes is important. However, inference of target gene regulation by miRNAs is difficult and is often achieved simply by investigating significant upregulation during cell senescence. Here, we inferred the regulation of target genes by miRNAs, using the recently developed MiRaGE server, together with the change in miRNA expression during fibroblast IMR90 cell senescence. We revealed that the simultaneous consideration of 2 criteria, the up(down) regulation and the down(up) regulatiion of target genes, yields more feasible miRNA, i.e., those that are most frequently reported to be down/upregulated and/or to possess biological backgrounds that induce cell senescence. Thus, when analyzing miRNAs that critically contribute to cell senescence, it is important to consider the level of target gene regulation, simultaneously with the change in miRNA expression.

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  • MiRaGE: Inference of Gene Expression Regulation via MicroRNA Transfection II Reviewed

    Y-H. Taguchi, Jun Yasuda

    BIO-INSPIRED COMPUTING AND APPLICATIONS   6840   129 - +   2012

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    How each microRNA regulates gene expression is unknown problem. Especially, which gene is targeted by each microRNA is mainly depicted via computational method, typically without biological/experimental validations. In this paper, we propose a new computational method, MiRaGE, to detect gene expression regulation via miRNAs by the use of expression profile data and miRNA target prediction. This method is tested to miRNA transfection experiments to tumor cells and succeeded in inference of transfected miRNA as only one miRNA with significant P-values for the first time.

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  • Principal Component Analysis for Bacterial Proteomic Analysis Reviewed

    Y-h. Taguchi, Akira Okamoto

    PATTERN RECOGNITION IN BIOINFORMATICS   7632   141 - 152   2012

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    Proteomic analysis is a very useful procedure to understand the bacterial behavioural responses to the external environmental factors. This is because bacterial genome information is mainly devoted to code enzyme for the control of the cellular metabolic networks. In this paper, we have performed proteomic analysis of Streptococcus pyogenes, which is known to be flesh-eating bacteria and can cause several human life-threatening diseases. Its proteome during growth phase is measured for four time points under two different culture conditions; with or without shaking. Its purpose is to understand the adaptivity to oxidative stresses. Principal component analysis is applied and turns out to be useful to depict biologically important proteins for both supernatant and cell components.

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  • Inference of Gene Regulation via miRNAs During ES Cell Differentiation Using MiRaGE Method Reviewed International journal

    Masato Yoshizawa, Y-h. Taguchi, Jun Yasuda

    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES   12 ( 12 )   9265 - 9276   2011.12

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    MicroRNA (miRNA) is a critical regulator of cell growth, differentiation, and development. To identify important miRNAs in a biological process, many bioinformatical tools have been developed. We have developed MiRaGE (MiRNA Ranking by Gene Expression) method to infer the regulation of gene expression by miRNAs from changes of gene expression profiles. The method does not require precedent array normalization. We applied the method to elucidate possibly important miRNAs during embryonic stem (ES) cell differentiation to neuronal cells and we infer that certain miRNAs, including miR-200 family, miR-429, miR-302 family, and miR-17-92 cluster members may be important to the maintenance of undifferentiated status in ES cells.

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  • Protein binding prediction using non-metric multidimensional scaling method Reviewed

    Hiromu Mogi, Y-h. Taguchi

    Bioinformatics and Biomedicine Workshops (BIBMW),2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS   950 - 952   2011.11

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  • The Analysis of DNA Shuffling by nMDS Reviewed

    Ryota Doi, Y-h. Taguchi

    2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS   1012 - 1013   2011

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    DNA shuffling is widely used for optimizing complex properties contained within DNA and proteins.
    However, success rate of it is deeply dependent upon which pair of DNAs is employed for DNA shuffling. In this paper, we have used non-metric multidimensional scaling (nMDS) to select best pair of DNAs for it. It turns out that nMDS can sometimes choose better pairs of DNAs than hierarchical clustering which is frequently used to select the suitable pair of DNAs.

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  • Principal Component Analysis for Bacterial Proteomic Analysis Reviewed

    Y-H. Taguchi, Akira Okamoto

    2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS   961 - 963   2011

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    Data-mining techniques are important for understanding biological phenotypes with large-scale datasets derived from comprehensive analysis such as shotgun proteomics. We attempted to illustrate differences of proteomic profiles among growth phase and cellular fractionation in Bacillus cereus by principal component analysis (PCA). In total, 10 proteins were picked up with significance biological phenotypes by PCA analysis. These results suggested that the PCA is useful tool for understanding proteomic analysis.

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  • Gene expression regulation during differenctiation from murine ES cells due to microRNA Reviewed

    Masato Yoshizawa, Yoshihiro Taguchi

    2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS   948 - 949   2011

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    we have infer gene expression regulation via microRNA using both gene expression profile and target gene tables during the differentiation from embryonic stem cell to neuronal cells. Comparison between obtained list of microRNA which regulates gene expression significantly and that of miRNAs whose expression level changes significantly results in significant overlap.

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  • Feature extraction for discriminance of symbiotic/parasitic bacterial type III effector protein using principal component analysis Reviewed

    Yuichi Nakano, Y. -H Taguchi

    2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS   964 - 965   2011

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    Type three secretion system (T3SS) is a protein machinery found in several bacteria. Using this machinery, bacteria infect the eukaryotic cell. The problem is that similar machinery can be found also in symbiotic bacteria. Thus, it is important if there are any difference between pathogenic and symbiotic T3SS. In this paper, we have investigated this difference using simple method, principal component analysis together with linear discriminant analysis. These two types proteins have clear difference between them.

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  • Tissue specific methylation and genotype Reviewed

    Ryoichi Kinoshita, Yoshihiro Taguchi

    2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS   953 - 955   2011

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    It is unclear how genotype affects methylation. In this paper, we have applied principal component analysis to both genotype and methylation on tumor, adjacent normal tissue, and blood DNA from 30 patients. Dominant pattern turns out to be upregulation from blood to tumor through tissue for both genotype and methylation. Overlap between significantly upregulating genotype and methylated genes are significantly overlaped. Thus, at least, for some regions, genotype correlates to methylation.

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  • Inference of Gene Expression Regulation via microRNA Transfection Reviewed

    Y-h Taguchi, Jun Yasuda

    ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS   6215   672 - +   2010

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    How each micioRNA regulates gene expiession is unknown problem Especially, which gent is targeted by each microRNA is mainly depicted vir computational method typically without biological/expenmental validations In this paper, we propose a computational method to detect gene expression regulation via mRNAs by the use of expiession profile data and mRNA target prediction This method is tested to mRNA tiansfection experiments to tuna cells and succeeded in inference of transfected mRNA

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  • Nonmetric distances for barcode of life Reviewed

    Hisamitsu Akiba, Y. H. Taguchi

    IPSJ Transactions on Bioinformatics   1   35 - 41   2008.11

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    The Barcode of Life (BOL) project aims to identify species with no other information than DNA sequence. We assume that BOL includes information on higher taxa. In the present study, we compute nonmetric distance from BOL barcodes by using rank order of pairwise distance for 3 distinct examples, namely, Ant Diversity in Northern Madagascar, Survey of Chelicerates, and Birds of North America. This enables us to recognize higher taxa, i.e., genus, family, and order, more easily. For example, the ratio of mean inner taxa nonmetric distance to the intertaxa distance is smaller than that for raw (metric) distance. Furthermore, for most pairs of higher taxa, the mean intertaxa distance is more than twice larger than intrataxa distances. The nonmetric multidimensional scaling method enables to discriminate higher taxa compared to tree construction by the neighbor-joining method or the maximum parsimony method with raw distance measure, when each species is embedded into more than 40 dimensional space with an accuracy of 90% even after leave-oneout- cross-validation. © 2008 Information Processing Society of Japan.

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  • Gene Ontology term prediction based upon amino acid occurrence Reviewed

    Y-h. Taguchi, M. Michael Gromiha

    2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8   615 - 620   2008

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    Usually prediction of molecular functions of proteins from their amino acid sequences is based upon sequence similarity with proteins of known functions. However, it is well known that function is mainly dependent upon protein structures than sequences. Since structures are often independent of sequences, it is important to predict function without sequence similarities. Here we propose a method based upon amino acid occurrence for predicting Gene Ontology (GO) term. We have tested the method in a set of 3212 proteins in Protein Data Bank with less than 40% sequence identity. Our method achieved more than 50% sensitivity and 20% precision for c.a. 20 selected GO terms among the most frequent 557 GO terms. Mean sensitivity, specificity, precision, and accuracy for relatively rare (but majority) 402 GO terms among the 557 GO terms are 13%, 99%, 9% and 99%, respectively. They are significantly larger than expected values of less than 2% under assuming random selection.

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  • Application of amino acid occurrence for discriminating different folding types of globular proteins Reviewed International journal

    Y-h Taguchi, M. Michael Gromiha

    BMC BIOINFORMATICS   8 ( 404 )   2007.10

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    Background: Predicting the three-dimensional structure of a protein from its amino acid sequence is a long-standing goal in computational/ molecular biology. The discrimination of different structural classes and folding types are intermediate steps in protein structure prediction.
    Results: In this work, we have proposed a method based on linear discriminant analysis (LDA) for discriminating 30 different folding types of globular proteins using amino acid occurrence. Our method was tested with a non-redundant set of 1612 proteins and it discriminated them with the accuracy of 38%, which is comparable to or better than other methods in the literature. A web server has been developed for discriminating the folding type of a query protein from its amino acid sequence and it is available at http:// granular. com/ PROLDA/.
    Conclusion: Amino acid occurrence has been successfully used to discriminate different folding types of globular proteins. The discrimination accuracy obtained with amino acid occurrence is better than that obtained with amino acid composition and/or amino acid properties. In addition, the method is very fast to obtain the results.

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  • Protein fold recognition based upon the amino acid occurrence Reviewed

    Y. -H. Taguchi, M. Michael Gromiha

    PATTERN RECOGNITION IN BIOINFORMATICS, PROCEEDINGS   4774   120 - 131   2007

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    We have investigated the relative performance of amino acid occurrence and other features, such as predicted secondaxy structure, hydrophobicity, normalized van der Waals volume, polarity, polaxizability, and real/predicted contact information of residues, for recognizing protein folds. We observed that the improvement over other features is only marginal compared with amino acid occurrence. This is because amino acid occurrence, indirectly, can consider varieties of physical properties which are useful to discriminate protein folds. If we consider only proteins which are well aligned structurally with each other, the accuracy of discrimination is drastically improved. In order to discriminate protein folds more accurately, we need to consider anything other than structure alignment.

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  • Temperature measurement under convection and segregation in a vibrated bed of powder: A numerical study Reviewed International journal

    S Kiyono, YH Taguchi

    GRANULAR MATTER   8 ( 1 )   27 - 33   2006.3

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    In numerically simulated vibrated beds of powder, we measure temperature under convection by the generalized Einstein's relation. The spatial temperature distribution turns out to be quite uniform except for the boundary layers. In addition to this, temperature remains uniform even if segregation occurs. This suggests the possibility that there exists some thermal equilibrium state even in a vibrated bed of powder. This finding may lead to a unified view of the dynamic steady state of granular matter.

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  • Some implications of renormalization group theoretical ideas to statistics Reviewed International coauthorship International journal

    S Rajaram, YH Taguchi, Y Oono

    PHYSICA D-NONLINEAR PHENOMENA   205 ( 1-4 )   207 - 214   2005.6

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    Renormalization group (RG) theory and statistical data analysis are obviously closely related. Reductive RG can provide a natural framework to understand asymptotic estimate problems. Some observations relevant to this problem and an example of a data-mining algorithm inspired by the consideration are exhibited. (c) 2004 Elsevier B.V. All rights reserved.

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  • Relational patterns of gene expression via non-metric multidimensional scaling analysis Reviewed International coauthorship International journal

    Y Taguchi, Y Oono

    BIOINFORMATICS   21 ( 6 )   730 - 740   2005.3

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    Motivation: Microarray experiments result in large-scale data sets that require extensive mining and refining to extract useful information. We demonstrate the usefulness of (non-metric) multidimensional scaling (MDS) method in analyzing a large number of genes. Applying MDS to the microarray data is certainly not new, but the existing works are all on small numbers (&lt; 100) of points to be analyzed. We have been developing an efficient novel algorithm for non-metric MDS (nMDS) analysis for very large data sets as a maximally unsupervised data mining device. We wish to demonstrate its usefulness in the context of bioinformatics (unraveling relational patterns among genes from time series data in this paper).
    Results: The Pearson correlation coefficient with its sign flipped is used to measure the dissimilarity of the gene activities in transcriptional response of cell-cycle-synchronized human fibroblasts to serum. These dissimilarity data have been analyzed with our nMDS algorithm to produce an almost circular relational pattern of the genes. The obtained pattern expresses a temporal order in the data in this example; the temporal expression pattern of the genes rotates along this circular arrangement and is related to the cell cycle. For the data we analyze in this paper we observe the following. If an appropriate preparation procedure is applied to the original data set, linear methods such as the principal component analysis (PCA) could achieve reasonable results, but without data preprocessing linear methods such as PCA cannot achieve a useful picture. Furthermore, even with an appropriate data preprocessing, the outcomes of linear procedures are not as clear-cut as those by nMDS without preprocessing.

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  • Why Do Physicists Study Barchan Dunes? Reviewed

    Y-h. Taguchi

    JPSJ News and Comments   2005.1

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  • NONMETRIC MULTIDIMENSIONAL SCALING AS A DATA-MINING TOOL: NEW ALGORITHM AND NEW TARGETS Reviewed

    Y-H. Taguchi, Yoshitsugu Oono

    GEOMETRIC STRUCTURES OF PHASE SPACE IN MULTIDIMENSIONAL CHAOS: APPLICATIONS TO CHEMICAL REACTION DYNAMICS IN COMPLEX SYSTEMS, PT B   130 ( PartB )   315 - 351   2005

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    DOI: 10.1002/0471712531.ch18

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  • A toy model of flying snake's glide Reviewed International journal

    K Matsumura, YH Taguchi

    JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN   72 ( 11 )   3002 - 3005   2003.11

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    We have developed a toy model of flying snake's glide [J. J. Socha: Nature 418 (2002) 603] by modifying a model for a falling paper. We have found that asymmetric oscillation is a key about why snake can glide. Further investigation for snake's glide will provide us details about how it can glide without a wing.

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  • Verifying relationship between height and spacing, in Barchan dunes simulated by the Coupled Map Lattice model Reviewed International journal

    J Shibata, YH Taguchi

    JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN   72 ( 10 )   2685 - 2689   2003.10

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    We have investigated the relationship between height and spacing of Barchan dunes which the coupled map lattice model numerically generates. There is a scaling relation between them and the values of the scaling exponents agree well with real dunes' values. The values of these scaling exponents are the same for both steady states and transient states.

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  • New Phases in Optimal Velocity Model Reviewed

    Yuusaku Suzuki, Y-h. Taguchi

    {IFAC} Proceedings Volumes   36 ( 14 )   395 - 399   2003

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    10th \{IFAC\} Symposium on Control in Transportation Systems 2003, Tokyo, Japan, 4-6 August 2003

    DOI: 10.1016/s1474-6670(17)32452-7

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  • Analysis of segregation property of burden using 2-dimensional discrete model Reviewed

    S Matsuzaki, Y Taguchi

    TETSU TO HAGANE-JOURNAL OF THE IRON AND STEEL INSTITUTE OF JAPAN   88 ( 12 )   823 - 830   2002.12

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    A segregation phenomenon observed when particles having different density and shape as well as particle diameter have been charged by mixing them were examined with the use of model experimental apparatus and a discrete simulation model. In consequence of it, it has come out that the segregation phenomenon of mixed particles depends on both of the particle diameter ratio and density ratio with a base particle and dependency can be estimated with a theoretical model.

    DOI: 10.2355/tetsutohagane1955.88.12_823

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  • Numerical investigation of surface level instability due to a tube in a vibrating bed of powder Reviewed

    Y Maeno

    PHYSICA A   232 ( 1-2 )   27 - 39   1996.10

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    We investigate numerically surface level instability originally found in experiments when a tube is injected into a vibrating bed of powder. It turns out that a thicker (thinner) tube makes the surface level inside the tube higher (lower) than the surface level outside the tube. With a fixed acceleration amplitude of vibration, the surface level inside the tube becomes higher as the amplitude of vibration increases, which can be explained by considering the dependence of flow patterns upon control parameters.

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  • Fractal limit distributions in random transports Reviewed International journal

    H Takayasu, T Kawakami, YH Taguchi, T Katsuyama

    FRACTALS-AN INTERDISCIPLINARY JOURNAL ON THE COMPLEX GEOMETRY OF NATURE   4 ( 3 )   257 - 264   1996.9

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    We analyze a random transport model of a scalar quantity on a discrete space-time. By changing a parameter which is a portion of the quantity transported at a time, we observe a continuous change of steady-state distribution of fluctuations from Gaussian to a power-law when the mean value of the scalar quantity is not zero. In the symmetric case with zero mean, the steady-state converges either to a trivial no fluctuation state or to a Lorentzian fluctuation state with diverging variance independent of the parameter. We discuss a possible origin of the intermittent behaviors of fully-developed fluid turbulence as an application.

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  • POWER-LAW VELOCITY FLUCTUATIONS DUE TO INELASTIC-COLLISIONS IN NUMERICALLY SIMULATED VIBRATED BED OF POWDER Reviewed International journal

    YH TAGUCHI, H TAKAYASU

    EUROPHYSICS LETTERS   30 ( 8 )   499 - 504   1995.6

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    Distribution functions of relative velocities among particles in a vibrated bed of powder are studied both numerically and theoretically. In the solid phase where granular particles remain near their local stable states, the probability distribution is Gaussian. On the other hand, in the fluidized phase, where the particles can exchange their positions, the distribution clearly deviates from Gaussian. This is interpreted with two analogies: aggregation processes and soft-to-hard turbulence transition in thermal convection. The non-Gaussian distribution is well approximated by the t-distribution which is derived theoretically by considering the effect of clustering by inelastic collisions in the former analogy.

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  • DYNAMICS OF GRANULAR MATTER Reviewed International journal

    H HAYAKAWA, H NISHIMORI, S SASA, Y TAGUCHI

    JAPANESE JOURNAL OF APPLIED PHYSICS PART 1-REGULAR PAPERS SHORT NOTES & REVIEW PAPERS   34 ( 2A )   397 - 408   1995.2

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    Granular matter is a typical example of a new topic in statistical (phenomenology) mechanics. Reconsidering granular matter from the physical point of view, several new aspects have been clarified, although granular matter has been studied by engineers for a long (period of) time. This review examines three topics: (1) pattern dynamics of sand ripples and dunes, (2) mathematical structure of a fluidized bed, and (3) convection and turbulence in a Librating bed. investigating these topics, it is found that the dynamics of granular matter exhibits many typical nonlinear phenomena, for example, formations of pattern. localized states. and turbulence.

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  • NUMERICAL STUDY OF GRANULAR TURBULENCE AND THE APPEARANCE OF THE K(-5)/(3) ENERGY-SPECTRUM WITHOUT FLOW Reviewed International journal

    YH TAGUCHI

    PHYSICA D   80 ( 1-2 )   61 - 71   1995.1

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    A vibrated bed of powder, modeled by a vessel filled with monodisperse glass beads and shaken by a loud speaker, is investigated numerically with the distinct element method of molecular dynamics. When the bed is shaken vigourously, the displacement vectors of powder particles have a power spectrum that depends upon the wavenumber k as k-5/3. This dependence originates in the balance of the injected and dissipative energy, analogous to Kolmogorov's proposal to explain the k-5/3 energy spectrum observed in the fluid turbulence. Furthermore, the same spectrum still appears even without flow in the powder. Thus Kolmogorov's argument appears to be more universal than believed before.

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  • NON-GAUSSIAN DISTRIBUTION IN RANDOM TRANSPORT DYNAMICS Reviewed International journal

    M TAKAYASU, H TAKAYASU, YH TAGUCHI

    INTERNATIONAL JOURNAL OF MODERN PHYSICS B   8 ( 28 )   3887 - 3961   1994.12

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    Statistical properties of random transport models defined on discrete space-time are investigated both numerically and analytically. As an extreme limit we first consider aggregation limit of massive particles. With the presence of permanent injection we have a nontrivial steady state where the mass distribution follows a power law. It is shown that the steady state is universal and very robust. Next, we analyze the cases of imperfect aggregation that a finite portion is transported at a time. We have a Gaussian fluctuation governed by the ordinary diffusion equation in the nonaggregation limit, while the system converges to the power law steady state in the aggregation limit even without injection. In the intermediate cases the fluctuations are always between Gaussian and the power law. Underlying relations to the exponential-like distributions in fluid turbulence are also discussed.

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  • TURBULENT FLOW IN VIBRATED BED OF POWDER: NEW TARGET TO INVESTIGATE TURBULENT FLOW Reviewed

    Y-h. Taguchi

    Fractals   636 - 641   1994.10

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  • Numerical modeling of morphological changes of the fracture propagation in the quenched glass plate Reviewed International journal

    Y-h.Taguchi

    Mod. Phys. Lett. B   1335 - 1342   1994.9

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  • Dynamical modelling of fracture propagation Reviewed International journal

    Y. H. Taguchi

    Materials Science and Engineering A   176 ( 1-2 )   295 - 298   1994.3

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    The numerical modelling of fracture propagation is proposed. Material is modelled as a triangular lattice whose bonds have short-range interaction. The interaction models the elastic properties of materials. Using a gaussian-type interaction, it is possible to describe fracture propagation without introducing a breakdown threshold. This enables us to simulate fracture propagation effectively. The simulation exhibits a morphological transition between branching and non-branching fracture propagation. © 1994.

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  • NUMERICAL MODELING OF CONVECTIVE MOTION IN GRANULAR-MATERIALS Reviewed

    YH TAGUCHI

    ADVANCED POWDER TECHNOLOGY   5 ( 3 )   297 - 303   1994

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    Numerical modeling of convection of powder was performed. Convection of powder occurs when a strong vertical vibration is applied. When the control parameter, the acceleration amplitude GAMMA, exceeds some critical value GAMMA(c), convection and heaping start. In our simulation, the powder is modeled with a discrete elementary method but without rotation and with isotropic linear visco-elastic interaction. The convection is reproduced and GAMMA(c) turns out to be about the gravity acceleration g, which agrees with experimental findings. Another remarkable phenomena, surface fluidization, is also reproduced and the critical value GAMMA(c) again agrees with experimental findings. It is suggested that this convection is caused by the instability of powder induced by the elastic interaction.

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  • A NEW MESOSCOPIC SCALE-MODEL FOR SIMULATING FLUID TURBULENCE - THE LATTICE VORTEX TUBE MODEL Reviewed International journal

    Y TAGUCHI, H TAKAYASU

    PHYSICA D-NONLINEAR PHENOMENA   69 ( 3-4 )   366 - 379   1993.12

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    Fluid turbulence is analysed with a lattice model which requires relatively smalt number of degrees of freedom. The lattice model employs vortex tube representation and can reproduce characteristic features in turbulence; Kolmogorov's inertial range is observed, probability distribution functions of local Velocities are close to a Gaussian, exponential-like distributions appear in local vorticity, relative velocities and local velocity through a high-pass filter. Coherent structures consisted of vortex tubes are also observed. The model also provides new mechanism which generates non-Gaussian distributions.

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  • TURBULENT FLOW IN VIBRATED BED OF POWDER: NEW TARGET TO INVESTIGATE TURBULENT FLOW Reviewed International journal

    Y. -H. Taguchi

    FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY   1 ( 4 )   1080 - 1085   1993.12

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    The flow patterns in the vibrated bed of powder are investigated numerically in two dimensions. They turn out to exhibit turbulent flow like that observed in fully developed turbulence. The power spectrum of their flow lines has k(-5/3) power, where k is the wave number. Since the numerical investigation of powder requires very few computational resources, it provides us with a new tool to study turbulence easily.

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  • K(-5/3) POWER SPECTRUM IN POWDER - TURBULENT-FLOW IN A VIBRATED BED - NUMERICAL RESULTS Reviewed International journal

    YH TAGUCHI

    EUROPHYSICS LETTERS   24 ( 3 )   203 - 209   1993.10

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    The numerical simulation of powder flow in vibrated beds exhibits a k-5/3 spectrum, where k is the wave number. Local motion in solid phase without flow also shows the same spectrum. This powder turbulence provides us with a powerful tool to investigate hard turbulence.

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  • WORKSHOP ON DYNAMICS OF POWDER SYSTEMS INSTITUTE OF STATISTICAL MATHEMATICS, TOKYO, NOVEMBER 16-18, 1992 - PREFACE Reviewed

    YH TAGUCHI, H HAYAKAWA, S SASA, H NISHIMORI

    INTERNATIONAL JOURNAL OF MODERN PHYSICS B   7 ( 9-10 )   R3 - R4   1993.4

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  • NUMERICAL MODELING OF VIBRATED BEDS Reviewed International journal

    YH TAGUCHI

    INTERNATIONAL JOURNAL OF MODERN PHYSICS B   7 ( 9-10 )   1839 - 1858   1993.4

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    I propose a numerical model which describes the dynamical features of vibrated beds. It succeeds in reproducing convective motion in vibrated beds which was observed by Faraday for the first time in 1831. In addition to this, this modeling can explain threshold value of instability and surface fluidization. Moreover, I numerically show vibrated bed without side wall can exhibit strong non-linear feature like turbulence or anomalous diffusion.

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  • NON-GAUSSIAN DISTRIBUTION IN RANDOM ADVECTION DYNAMICS Reviewed International journal

    H TAKAYASU, YH TAGUCHI

    PHYSICAL REVIEW LETTERS   70 ( 6 )   782 - 785   1993.2

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    A discrete model of random transport is analyzed both numerically and theoretically. Non-Gaussian fluctuations are always realized when a finite portion is transported at a time. Gaussian and power-law fluctuations appear in extreme limits.

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  • POWDER TURBULENCE - DIRECT ONSET OF TURBULENT-FLOW Reviewed International journal

    YH TAGUCHI

    JOURNAL DE PHYSIQUE II   2 ( 12 )   2103 - 2114   1992.12

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    A vibrated bed of powder with a large aspect ratio (periodic boundary) is examined. In contrast to the bed with a small aspect ratio (with side wall), no clear convection roll is observed. The flow in the fluid phase seems to be always turbulent, because the local vibration of powder in static state has already become spatially random before the flow starts. This powder turbulence provides us with a new tool to study general hard turbulence.

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  • NEW ORIGIN OF A CONVECTIVE MOTION - ELASTICALLY INDUCED CONVECTION IN GRANULAR-MATERIALS Reviewed International journal

    YH TAGUCHI

    PHYSICAL REVIEW LETTERS   69 ( 9 )   1367 - 1370   1992.8

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    Convection and fluidization in a vibrated bed of powder are reproduced in a numerical simulation. In the simulation, each particle of the powder, during a collision, has a viscoelastic interaction with the other colliding particle. Because of the discreteness of the particles, this elasticity causes convection. The critical values of fluidization and convection agree with experiments.

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  • LORENTZIAN DISTRIBUTION OF INTERACTING VORTEX TUBES Reviewed International coauthorship International journal

    Y TAGUCHI, H TAKAYASU

    PHYSICAL REVIEW A   41 ( 4 )   2249 - 2251   1990.2

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    DOI: 10.1103/PhysRevA.41.2249

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  • FRACTURE PROPAGATION GOVERNED BY THE LAPLACE EQUATION Reviewed International journal

    YH TAGUCHI

    PHYSICA A   156 ( 3 )   741 - 755   1989.4

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    DOI: 10.1016/0378-4371(89)90018-6

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  • AGGREGATION OF PARTICLES WHICH MOVE ON DETERMINISTIC TRAJECTORIES WITH FRACTAL DIMENSION-2 .1. A SIMPLE AND NEW MODEL FOR DLA Reviewed International journal

    YH TAGUCHI

    JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL   21 ( 22 )   4235 - 4240   1988.11

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  • SELF-AVOIDING WALKS ON SIERPINSKI CARPETS Reviewed International journal

    YH TAGUCHI

    JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL   21 ( 8 )   1929 - 1935   1988.4

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  • NON-INTEGER-DIMENSIONAL HYPER-EUCLIDEAN LATTICES ON SIERPINSKI CARPETS Reviewed International journal

    Y TAGUCHI

    JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL   21 ( 3 )   855 - 857   1988.2

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    DOI: 10.1088/0305-4470/21/3/043

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  • LACUNARITY AND UNIVERSALITY Reviewed International journal

    Y TAGUCHI

    JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL   20 ( 18 )   6611 - 6616   1987.12

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    DOI: 10.1088/0305-4470/20/18/058

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  • THE PHASE-DIAGRAM OF A LATTICE GAS-MODEL FOR BETA-''-ALUMINA Reviewed International journal

    T ISHIKAWA, Y TAGUCHI, Y OZEKI

    JOURNAL OF PHYSICS C-SOLID STATE PHYSICS   20 ( 30 )   4909 - 4916   1987.10

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  • THE CORRELATION-FUNCTION OF THE +/-J MODEL ON THE FINITE SQUARE AND SIMPLE CUBIC LATTICES Reviewed

    T OGUCHI, Y TAGUCHI

    PROGRESS OF THEORETICAL PHYSICS   77 ( 4 )   775 - 780   1987.4

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  • Transfer Matrix and Finite-Size Scaling for the Ising Model on Two- and Three-Dimensional Lattices : International journal

    OGUCHI Takehiko, TAGUCHI Yoshihiro

    Progress of theoretical physics. Supplement   ( 87 )   23 - 32   1987

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    The transfer matrices for the ferromagnetic Ising model on the two- and three-dimensional lattices with finite size are formulated using the screw method introduced by Kramers and Wannier, and the largest and next largest eigenvalues of matrices are obtained by a computer. Then the specific heats and magnetic susceptibilities are calculated for each system with various sizes. By use of the finite-size scaling and Roomany-Wyld approximations, the critical exponents of the infinite-size systems are evaluated. They are in quite good agreement with the reliable ones.

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  • A NUMERICAL STUDY OF SPIN-1/2 ALTERNATING ANTIFERROMAGNETIC HEISENBERG LINEAR-CHAINS Reviewed International journal

    K OKAMOTO, H NISHIMORI, Y TAGUCHI

    JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN   55 ( 5 )   1458 - 1465   1986.5

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  • GROUND-STATE OF QUANTUM SPIN-GLASS WITH INFINITE RANGE INTERACTIONS Reviewed International journal

    H NISHIMORI, Y TAGUCHI, T OGUCHI

    JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN   55 ( 2 )   656 - 659   1986.2

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  • GROUND-STATE PROPERTIES OF THE HEISENBERG-ANTIFERROMAGNET - NUMERICAL STUDY Reviewed

    T OGUCHI, H NISHIMORI, Y TAGUCHI

    JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS   54-7   1353 - 1354   1986.2

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  • GROUND-STATE OF ANTIFERROMAGNETIC QUANTUM SPIN SYSTEMS ON THE TRIANGULAR LATTICE Reviewed International journal

    T OGUCHI, H NISHIMORI, Y TAGUCHI

    JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN   55 ( 1 )   323 - 330   1986.1

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    DOI: 10.1143/JPSJ.55.323

    DOI: 10.1143/jpsj.55.323

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  • TRANSFER-MATRIX AND FINITE-SIZE SCALING FOR THE ISING-MODEL ON TWO-DIMENSIONAL AND 3-DIMENSIONAL LATTICES International journal

    T OGUCHI, Y TAGUCHI

    PROGRESS OF THEORETICAL PHYSICS SUPPLEMENT   87 ( 87 )   23 - 32   1986

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    DOI: 10.1143/PTPS.87.23

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  • NUMERICAL DIAGONALIZATION OF QUANTUM SPIN HAMILTONIANS Reviewed International journal

    H NISHIMORI, Y TAGUCHI

    PROGRESS OF THEORETICAL PHYSICS SUPPLEMENT   ( 87 )   247 - 255   1986

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:KYOTO UNIV  

    DOI: 10.1143/PTPS.87.247

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  • THE SPIN-WAVE THEORY IN ANTIFERROMAGNETIC HEISENBERG-MODEL ON FACE-CENTERED CUBIC LATTICE Reviewed International journal

    T OGUCHI, H NISHIMORI, Y TAGUCHI

    JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN   54 ( 12 )   4494 - 4497   1985.12

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    DOI: 10.1143/JPSJ.54.4494

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    Other Link: http://orcid.org/0000-0003-0867-8986

  • Novel Method for Detection of Genes With Altered Expression Caused by Coronavirus Infection and Screening of Candidate Drugs for SARS-CoV-2

    Y-h. Taguchi

  • Novel Method for Detection of Genes With Altered Expression Caused by Coronavirus Infection and Screening of Candidate Drugs for SARS-CoV-2

    Y-h. Taguchi

  • Novel Method for Detection of Genes With Altered Expression Caused by Coronavirus Infection and Screening of Candidate Drugs for SARS-CoV-2

    Y-h. Taguchi

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Books

  • 学び直し高校物理 挫折者のための超入門 (講談社現代新書)

    田口 善弘( Role: Sole author)

    講談社  2024.2  ( ISBN:4065346924

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    Total pages:272   Book type:General book, introductory book for general audience

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  • MicroRNA: From Bench to Bedside

    Y-h. Taguchi( Role: ContributorChapter 10 RNA m6A modification and microRNAs)

    Elsevier (Academic Press)  2022.7  ( ISBN:9780323897747

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    Total pages:654   Language:English   Book type:Scholarly book

    DOI: 10.1016/B978-0-323-89774-7.00020-0

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    Other Link: https://www.sciencedirect.com/science/article/pii/B9780323897747000200

  • Handbook of Machine Learning Applications for Genomics

    Y-h. Taguchi( Role: ContributorMultiomics Data Analysis of Cancers Using Tensor Decomposition and Principal Component Analysis Based Unsupervised Feature Extraction, Pages 1-17; In Silico Drug Discovery Using Tensor Decomposition Based Unsupervised Feature Extraction, Pages 101-120; Sincle Cell RNA-seq Analysis Using Tensor Decomposition and Principal Component Analysis Based Unsupervised Feature Extraction, Pages 155-176)

    Springer  2022.6  ( ISBN:9789811691577

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    Total pages:218   Responsible for pages:1-17,101-120,155-176   Language:English   Book type:Scholarly book

    DOI: 10.1007/978-981-16-9158-4

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  • Handbook of Machine Learning Applications for Genomics

    Sanjiban Sekhar Roy, Y.-H. Taguchi( Role: Joint editor)

    Springer  2022.6  ( ISBN:9789811691577

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    Total pages:212   Responsible for pages:1-17,101-120,155-176   Language:English   Book type:Scholarly book

    DOI: 10.1007/978-981-16-9158-4

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  • 疾患原因遺伝子・タンパク質の 解析技術と創薬/診断技術への応用

    田口善弘( Role: Contributor第1章11節 機械学習による遺伝子情報の解析技術と医薬品開発への応用)

    技術情報協会  2022.3  ( ISBN:9784861048777

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    Total pages:530   Responsible for pages:108-114   Language:Japanese   Book type:Scholarly book

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  • はじめての機械学習 : 中学数学でわかるAIのエッセンス

    田口, 善弘

    講談社  2021.7  ( ISBN:9784065239605

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    Total pages:217p   Language:Japanese  

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  • Data Analytics in Biomedical Engineering and Healthcare

    Y.-H.Taguchi, Hsiuying Wang( Role: ContributorChapter 8 : Application of PCA based unsupervised FE to neurodegenerative diseases)

    Elsevier  2020.10  ( ISBN:9780128193143

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    Total pages:292   Responsible for pages:131-144  

    DOI: 10.1016/B978-0-12-819314-3.00008-2

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  • 生命はデジタルでできている 情報から見た新しい生命像 (ブルーバックス)

    田口善弘( Role: Sole author)

    講談社  2020.5  ( ISBN:4065195977

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    Total pages:240   Language:Japanese   Book type:General book, introductory book for general audience

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  • Application of Omics, AI and Blockchain in Bioinformatics Research

    Y-h. TAGUCHI( Role: ContributorChapter 10: Tensor Decomposition Based Unsupervised Feature Extraction Applied to Bioinformatics)

    World Scientific  2019.10  ( ISBN:9789811203572

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    Total pages:208   Responsible for pages:159-187   Language:English   Book type:Scholarly book

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  • Unsupervised Feature Extraction Applied to Bioinformatics: A PCA Based and TD Based Approach

    Y-h. TAGUCHI( Role: Sole author)

    Springer International Publishing  2019.8  ( ISBN:9783030224554

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    Total pages:X. 314   Language:English   Book type:Scholarly book

    This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. Allows readers to analyze data sets with small samples and many features; Provides a fast algorithm, based upon linear algebra, to analyze big data; Includes several applications to multi-view data analyses, with a focus on bioinformatics.

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  • Regulatory microRNA

    Y-h. Taguchi, Hsiuying Wang( Role: Edit)

    MDPI  2019.4  ( ISBN:9783038977681

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    Total pages:348   Language:English   Book type:Scholarly book

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  • Encyclopedia of Bioinformatics and Computational Biology

    Y-h. TAGUCHI( Role: ContributorRegulation of Gene Expression; Comparative Transcriptomics Analysis)

    Elsevier  2018.8  ( ISBN:9780128114322

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    Total pages:3284   Responsible for pages:806-813,814-818   Language:English   Book type:Scholarly book

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    Other Link: https://doi.org/10.1016/b978-0-12-809633-8.20163-5

  • in silico創薬におけるスクリーニングの高速化・高精度化技術

    田口 善弘, 岩舘満雄, 梅山秀明( Role: Contributor6.1節 FAMS を用いたタンパク質機能予測に基づくDrugDiscovery)

    技術情報協会  2018.1  ( ISBN:9784861046889

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    Total pages:540   Language:Japanese   Book type:Scholarly book

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  • Computational Methods with Applications in Bioinformatics Analysis

    Jeffrey J, P. Tsai, Ka-log Ng( Role: ContributorChapter 8: Principal component analysis based unsupervised feature extraction applied to bioinformatics analysis)

    World Scientific  2017.6  ( ISBN:9789813207974

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  • 事例で学ぶ数学活用法

    大熊, 政明, 金子, 成彦, 吉田, 英生( Role: Contributor)

    朝倉書店  2015.2  ( ISBN:9784254111422

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    Total pages:viii, 291p   Language:Japanese  

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  • Big Data Analytics in Bioinformatics and Healthcare

    Y-H. Taguchi, Mitsuo Iwadate, Hideaki Umeyama, Yoshiki Murakami, Akira Okamoto( Role: ContributorChapter 7: Heuristic Principal Component Analysis-Based Unsupervised Feature Extraction and Its Application in Binoinformatics)

    IGI Global  2014.10  ( ISBN:9781466666115

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    Total pages:528   Responsible for pages:138-162   Language:English   Book type:Scholarly book

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  • 高校で教わりたかった物理

    田口 善弘( Role: Sole author)

    日本評論社  2009.2  ( ISBN:9784535600324

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  • ブックガイド : 文庫で読む科学

    岩波書店編集部( Role: Contributor科学的思考法のすすめ)

    岩波書店  2007.6  ( ISBN:9784000074728

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    Total pages:iv, 114p   Language:Japanese  

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  • New Phases in Optimal velocity Model

    Elsevier Science A Proceedings Volume From The 10th IFAC Symposium, Tokyo, Japan, 4-6 August 2003 (IFAC Proceedings S.)  2004.6 

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  • 複雑性のキーワード

    田口 善弘, 三井 秀樹, 高木 英行( Role: ContributorPart1 複雑系オーバービュー)

    共立出版  2000.2  ( ISBN:4320016424

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    Total pages:160   Language:Japanese   Book type:Textbook, survey, introduction

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  • インターネット時代の数学

    戸川 隼人, 中嶋 正之, 杉原 厚吉, 野寺 隆志( Role: Contributor7章 複雑系)

    共立出版  1999.1  ( ISBN:9784320029170

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    Total pages:266   Responsible for pages:227-235   Book type:Scholarly book

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  • 粉粒体の物理学

    西森 拓, 早川 尚男, 田口 善弘

    日本物理学会  1999 

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  • 粉体シミュレーション入門

    粉体工学会( Role: Contributor3.6粉体振動層の流動挙動)

    産業図書  1998.3 

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    Total pages:196   Responsible for pages:61-66   Book type:Scholarly book

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  • 砂時計の七不思議 : 粉粒体の動力学

    田口 善弘

    中央公論社  1995  ( ISBN:4121012682

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MISC

  • TDbasedUFEを用いた黄色ブドウ球菌敗血症におけるメチシリン耐性の解析

    渡邉 将太郎, 田口 善弘

    情報処理学会研究報告   2024-BIO-77 ( 11 )   1 - 6   2024.3

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    Authorship:Last author   Language:Japanese   Publishing type:Internal/External technical report, pre-print, etc.   Publisher:情報処理学会  

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  • カーネルテンソル分解ベースの教師なし特徴抽出を用いた百日咳ワクチンデータにおけるヒト遺伝子発現と抗体量の統合解析

    梅木 悠加, 田口 善弘

    情報処理学会研究報告   2024-BIO-77 ( 13 )   1 - 5   2024.3

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  • 教師なしテンソル分解に基づく、有糸分裂後の転写再活性化におけるヒストン修飾ブックマークとしての転写因子候補の抽出法 International coauthorship

    田口 善弘, ターキー ターキー

    情報処理学会研究報告   2024-BIO-77 ( 10 )   1 - 6   2024.3

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    Authorship:Lead author, Corresponding author   Language:Japanese   Publishing type:Internal/External technical report, pre-print, etc.   Publisher:情報処理学会  

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  • テンソル分解を用いた変数選択によるメチオニン依存性黒色腫細胞と非依存黒色腫細胞の遺伝子選択

    小林 謙太, 田口 善弘

    情報処理学会研究報告   2024-BIO-77 ( 12 )   1 - 6   2024.3

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    Authorship:Last author   Language:Japanese   Publishing type:Internal/External technical report, pre-print, etc.   Publisher:情報処理学会  

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  • Drug repositioning using multiple gene expression profiles International journal

    Y-h. Taguchi

    Open Access Government   41 ( 1 )   252 - 253   2024.1

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    Drug repositioning using multiple gene expression profiles

    Chuo University’s Professor Y-h. Taguchi places focus on drug repositioning using multiple gene expression profiles. In my previous manuscripts (1-3), I introduced our studies of in silico drug repositioning using gene expression profiles. Nevertheless, in these studies, we could use the single gene expression profile to perform in silico drug repositioning. In this manuscript, the last one in this series, I introduce our recent study (4) in which we could use multiple gene expression profiles. Please see my previous article for more details about the in silico drug repositioning. (5)In the studies (1-3) introduced previously, I used tensor decomposition (TD) based unsupervised feature extraction (FE) (6,7), which was a mathematical/computational method for data processing and was recently implemented in Bioconductor packages. (8)

    DOI: 10.56367/oag-041-10651

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  • 生成AIの最近の動向と会計業務へのインパクト Invited

    田口善弘

    中央大学会計人会会報別冊   35   2023.12

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    Authorship:Lead author, Last author, Corresponding author   Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (other)   Publisher:中央大学会計人会  

    File: bulletin35_other.pdf

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  • 遺伝子発現プロファイルに基づく新しい薬物間相互作用予測法

    田口善弘, ターキー・ターキー

    情報処理学会研究報告   2023-BIO-76 ( 3 )   1 - 4   2023.11

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  • Can we do drug repositioning without disease gene expression? International journal

    Y-h Taguchi

    Open Access Government   40 ( 1 )   282 - 283   2023.10

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    Can we do drug repositioning without disease gene expression?

    Chuo University’s Professor Y-h. Taguchi examines the application of cutting-edge single-cell-based measurements in drug repositioning. Single-cell analysis is a new trend in genomic science. Prior to its development, measurements were made on whole tissues. However, because tissues are composed of millions of cells, measuring them can miss important information. Imagine trying to understand the economic status of a big city with millions of people based only on the average income. Even if two cities have the same average income, one might be a mixture of a small number of rich people and many poor people, while the other might be composed of people who have almost the same income. Without detailed information about the distribution of income, we would not be able to distinguish between the two cities from an economic point of view. In the same way, single-cell measurements have opened the door to a true understanding of the genomic state of living material. By measuring the gene expression of individual cells, we can identify the different cell types present in a tissue and their relative abundance. This information can be used to understand how tissues function and how they respond to different stimuli.

    DOI: 10.56367/oag-040-10651

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  • 国際会議開催報告 ICBCB2023

    日本バイオインフォマティクス学会 ニュースレター   43   3 - 5   2023.7

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    Authorship:Lead author, Corresponding author   Language:Japanese   Publishing type:Meeting report   Publisher:日本バイオインフォマティクス学会  

    File: nl43.pdf

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  • Drug repositioning without the gene expression of disease cells treated with drugs International journal

    Y-h Taguchi

    Open Access Government   39 ( 1 )   28 - 29   2023.7

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    Drug repositioning without the gene expression of disease cells treated with drugs

    Y-h. Taguchi, Professor at the Department of Physics, Chuo University in Japan, provides comments on drug repositioning without the gene expression of disease cells treated with various drugs. Finding new drugs to treat diseases is always tricky. For example, although we have an effective mRNA vaccine for COVID-19, no effective drugs to treat COVID-19 have yet been identified. Since the difficulty is due to experiential evaluation, it is better to have a computer-oriented method. But how? We have one proposal for this at the Department of Physics, Chuo University in Japan. In our previous Open Access Government article, we introduce how to use gene expression (1) with which several drugs were treated for drug repositioning. The problem with the studies described in the last piece is that the strategy could be used only for diseases whose gene expression is retrieved. In the study described in the previous article, we analyzed gene expression profiles of cancer cell lines. Thus, the drug repositioning we could identify was only those toward cancers. This is a little bit problematic. If we would like to perform drug repositioning for other diseases, we need to prepare cell lines made from cells of target diseases. Since cancer cells are abnormal in some sense, it is not difficult to add immortality to cancer cell lines. Thus, we can easily maintain cancer cell lines in a Petri dish, but it is not the case for other cells. In addition to this, it is often challenging to collect cells from target diseases. It is time-consuming to treat cells of target diseases with various drugs.

    DOI: 10.56367/oag-039-10651

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  • Bioconductor パッケージ,TDbasedUFEとTDbasedUFEadvの紹介

    田口善弘

    情報処理学会研究報告   2023-BIO-74 ( 45 )   1 - 5   2023.7

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  • The link between gene expression and machine learning

    Y-h. Taguchi

    Open Access Government   38 ( 1 )   296 - 297   2023.4

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    The link between gene expression and machine learning

    Professor Y-h. Taguchi uses tensor decomposition to identify genes associated with altered gene expression caused by drug treatment. Machine learning has attracted much interest lately. Last year, generative AIs such as stable diffusion, which can create beautiful images from a given prompt and ChatGPT, which can mimic human conversations very well, were developed. All these popular AIs are based on a machine learning technique called deep learning (DL), inspired by the human brain's structure, also known as neural networks. However, these DL-based generative AIs have a hidden problem: they require massive amounts of data to learn.

    DOI: 10.56367/oag-038-10651

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  • 「科学的に物事を考える人を増やしたい。」ー中央大学物理学科教授 田口善弘さんインタビュー

    吉田純

    Reraise News   2023.4

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  • 七番目の彼女がアレな件について

    田口善弘

    カクヨム   2023.4

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  • LLMは生物学で捉えられるものかもしれない Invited

    田口善弘

    Modern Times   2023.4

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    Authorship:Lead author, Last author, Corresponding author   Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)  

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  • TDbasedUFEadv Reviewed

    Y-h. Taguchi

    Bioconductor   3 ( 17 )   2023.4

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    DOI: 10.18129/B9.bioc.TDbasedUFEadv

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  • TDbasedUFE on Bioconductor 3.17 @ Bio”Pack”athon2023#3

    田口善弘

    ToGoTV   2023.3

  • テンソル分解に基づく変数選択を用いたメラノーマの 1 細胞遺伝子発現プロファイルからの遺伝子選択

    松田信周, 田口善弘

    Jxiv   2023.3

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    Language:Japanese   Publishing type:Internal/External technical report, pre-print, etc.   Publisher:科学技術振興機構  

    DOI: 10.51094/jxiv.336

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  • 生命はデジタルでできている Invited

    田口善弘

    Modern Times   2023.3

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    「《3月28日配信》シンギュラリティはすでに起きている? 予想を越えているAI技術とその空洞の中身」では、AIが意識を持つかどうかが論点になるかもしれない。そもそも現在のAIとはどのようなものなのか、機械学習を使って研究を進めてきた田口善弘氏はAIをどのように捉えているのかを紹介しておこう。

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  • テンソル分解に基づく教師なし学習による変数選択法に基づく低酸素時の遺伝子発現およびm6Aプロファイルの変化に関連する遺伝子の同定

    田口善弘, サンジバン シェカール ロイ

    情報処理学会研究報告   2023-BIO-73 ( 15 )   1 - 6   2023.3

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  • TDbasedUFE Reviewed

    Y-h. Taguchi

    Bioconductor   3 ( 17 )   2023.2

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    DOI: 10.18129/B9.bioc.TDbasedUFE

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  • Kernel Tensor Decomposition can improve the drug discovery process International journal

    Y-h Taguchi

    Open Access Government   37 ( 1 )   202 - 203   2023.1

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    Kernel Tensor Decomposition can improve the drug discovery process

    Kernel tensor decomposition and its use in drug discovery for SARS-CoV-2 was vital, however, due to its general method, it has the potential to be used for a wide range of future problems. Through the series of my previous articles (1-5) that target the general audience, I have been avoiding discussing mathematical details that non-expert cannot easily understand. Nevertheless, I discuss these in this article since it is seemingly the last one about my COVD-19 studies. As a researcher of bioinformatics, my main interest is in the mathematical side. Thus avoiding mathematics has already prevented me from explaining well what I am really interested in.

    DOI: 10.56367/oag-037-10026

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  • マルチオミックス:バイオインフォマティクスの基礎知識6 Invited

    田口善弘

    Technote   2022.12

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  • テンソル分解に基づく教師なし特徴抽出による新型コロナウイルス(SARS-CoV-2)に対する新しい先進的in silico創薬手法の提案

    田口善弘, ターキーターキー

    情報処理学会研究報告   2022-BIO-72 ( 1 )   1 - 6   2022.11

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    背景 : COVID-19 は,世界中の人間社会に影響を及ぼしている重要なパンデミックであり,有効な薬剤の開発が急務となっている.COVID-19 の治療に有用と思われる薬剤候補化合物は多数存在するが,その評価には時間とコストがかかる.したがって,実験的な検証に先立ち,有効な可能性のある薬剤を同定するためのスクリーニングが必要である.方法 : 本研究では,重症急性呼吸器症候群コロナウイルス 2 に感染した複数の肺がん細胞株の遺伝子発現プロファイルに,最近提案されたテンソル分解(TD)ベースの教師なし特徴抽出(FE)を適用した.TD-based unsupervised FE により選択された 163 遺伝子の発現を有意に変化させる薬剤候補化合物を同定した.結果:多数の薬剤のスクリーニングに成功し,その中には,C646,chelerythrine chloride,canertinib,BX-795,sorafenib,QL-X-138,radicol,A-443654 などの多くの抗ウイルス剤既知化合物が含まれていた.CGP-60474,アルボシディブ,ミトキサントロン,QL-XII-47,ゲルダナマシン,フルチカゾン,アトルバスタチン,ケルセチン,モテキサフィンガドリニウム,トロバフロキサシン,ドキシサイクリン,メロキシカム,ゲンタマイシン,ジブロモクロルメタンがありました.また,抗寄生虫薬として同定され,最近では SARS-CoV-2 の臨床試験に含まれるようになったイベルメクチンもスクリーニングで同定された.結論:我々の戦略でスクリーニングされた薬剤は,COVID-19 患者の治療に有効な候補となる可能性がある

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  • メタボロームの全て:バイオインフォマティクスの基礎知識5

    田口善弘

    Tech Note   2022.11

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  • タンパク質の全て:バイオインフォマティクスの基礎知識4 Invited

    田口善弘

    Tech Note   2022.10

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  • Slight changes can improve much for algorithms looking at gene expressions

    Y-h Taguchi

    Open Access Government   36 ( 1 )   130 - 131   2022.10

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    Slight changes can improve much for algorithms looking at gene expressions

    Although it is impossible to fully describe the details since it is highly mathematical, the improvement is really small. In our method, we consider the expression of human genes whose number is as many as a few tens of thousands when SARS- COV-2, a virus which causes COVID-19, infects human cells. Then we generate new variables that discriminate infected cells from not infected ones, by summing up gene expressions. Here, Y-h. Taguchi, a Professor at Chuo University, looks at the slight changes made to algorithms when looking at the COVID-19 virus and gene expressions.

    DOI: 10.56367/oag-036-10026

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  • RNAの全て:バイオインフォマティクスの基礎知識3 Invited

    田口善弘

    Tech Note   2022.9

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  • ゲノム:バイオインフォマティクスの基礎知識2 Invited

    田口善弘

    Tech Note   2022.9

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  • テンソル分解・主成分分析を用いた教師無し標準偏差最適化変数選択法の遺伝子発現プロファイル、メチル化プロファイル、ヒストン修飾解析への応用

    田口善弘

    人工知能学会研究会資料 人工知能基本問題研究会   121   9 - 14   2022.9

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    DOI: 10.11517/jsaifpai.121.0_07

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  • テンソル分解を用いた教師無し学習による変数選択法のN6-メチルアデノシンを介した転写因子,生物パスウェイ,疾患の同定への応用

    田口 善弘, デゥハランシー アキラ, マイケル グロミハ

    情報処理学会研究報告   2022-BIO-71 ( 1 )   1 - 8   2022.9

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    N6-メチルアデノシン(m6A)編集は,様々な生物学的プロセスに寄与することが知られている最も一般的な RNA 修飾である.しかし,m6A がどのようなメカニズムで転写を制御しているかは不明です.最近,m6A はヒストン修飾を介して転写を制御することが提案されましたが,このデータセットを用いた包括的な解析は行われていません.本研究では,マウス胚性幹細胞(mESC)とヒト癌細胞株(HEC-1-A) からなるデータセットにテンソル分解を用いた教師なし学習による変数選択法を適用し,ヒトとマウスで有意に重複する 2 組の遺伝子(2 種共通の遺伝子総数 16763 個のうち有意に重複する遺伝子 63 個)を特定することに成功した.これらの有意に重複する遺伝子は,両遺伝子セットから最大で 10% の遺伝子を占めている.これらの 2 つの遺伝子セットを用いて,m6A が誘導する可能性のある転写因子(TF),m6A が寄与する可能性のある生物学的過程,m6A が引き起こす可能性のある疾患を同定したが,これらは互いに大きく重複している.これらの結果は,2 つの独立したデータセットを用いて共通に同定されたため,これらの転写因子,生物学的過程,および疾患に関する結果は,非常に頑健で信頼できるものであると考えられる.この結果は,m6A がどのようなメカニズムで生物学的プロセスに寄与しているのかを理解するのに役立つと思われる.

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  • バイオインフォマティクスとは:バイオインフォマティクスの基礎知識1 Invited

    田口善弘

    Tech Note   2022.8

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  • テンソル分解を用いた赤血球遺伝子発現データの解析

    相川 隼人, 田口 善弘

    情報処理学会研究報告   2022-BIO-70 ( 55 )   1 - 3   2022.6

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  • テンソル分解を用いた教師なし学習による変数選択法の前立腺がんマルチオミックスデータ解析への応用

    田口善弘, ターキー ターキー

    情報処理学会研究報告   2022-BIO-70 ( 51 )   1 - 10   2022.6

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  • テンソル分解による百日咳の不活化した全菌体ワクチンと無細胞ワクチンの差を示す遺伝子の推定

    梅木 悠加, 田口 善弘

    情報処理学会研究報告   2022-BIO-70 ( 52 )   1 - 6   2022.6

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  • Identification of DNA Loops in the Genome by Multidimensional Scaling

    Ryo Ishibashi, Y-H. Taguchi

    2022-BIO-70 ( 53 )   1 - 5   2022.6

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  • m6Aのメチル化による内在性レトロウイルス異常が精神疾患に関与する可能性,関連する遺伝子について

    仙名 瑛斗, 田口 善弘

    情報処理学会研究報告   2022-BIO-70 ( 54 )   1 - 5   2022.6

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  • Is human blood better than cell lines as a COVID-19 infection model?

    Y-h. Taguchi

    35 ( 1 )   182 - 183   2022.6

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    Other Link: http://edition.pagesuite-professional.co.uk/Launch.aspx?EID=85f0d134-d2ec-4b73-b1ac-c5001c395a2a

  • Can mice be an effective model animal for Covid-19?

    Y-h. Taguchi

    34   112 - 113   2022.3

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    Other Link: https://www.nouvelles-du-monde.com/la-souris-peut-elle-etre-un-animal-modele-efficace-pour-le-covid-19/

  • 腎臓明細胞癌のmiRNA指標のテンソル分解による解析 International coauthorship

    田口 善弘, 呉 家樂

    情報処理学会研究報告   2022-BIO-69 ( 1 )   1 - 6   2022.3

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    腎臓(淡)明細胞癌(kidney renal clear cell carcinoma,KIRC)は腎細胞癌の8割ほどを占めるがんであり、その原因を解明することは重要である。今回は我々はmRNAとmiRNAの発現量をがんと正常細胞で比較することで病因に関係すると目される遺伝子とmiRNAを複数個選ぶことに成功した。これらの遺伝子は先行研究でがんで有意に発現が変化していることが知られているだけではなく、KIRC患者の生存率に関係している遺伝子が多く含まれていた。またmiRNAについては、その標的遺伝子ががんに関係していることが解った。さらに選択された遺伝子やmiRNAは独立な2つの研究(それぞれが共通した患者についてmRNAとmiRNAの発現を計測している)において有意に重なりがある形で選択されることがわかったが、t検定、SAM、limmaなどの既存手法ではこのようなデータセットに依らないロバストな遺伝子やmiRNAの選択は出来なかった。

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  • お届けInfo 「顔認証システムの倫理的課題について」 Invited

    田口善弘

    お届けInfo   2022.3

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  • これだけは読んでおきたい! 機械学習を学ぶ人のための厳選入門書 Invited

    田口善弘

    講談社ブルーバックス広報サイト   2022.1

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  • 自動翻訳、ゲーム、画像処理…「機械学習」がやってるたった1つのこと Invited

    田口善弘

    講談社ブルーバックス広報サイト   2022.1

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  • How to compete with COVID-19 with a computer?

    Y-h. Taguchi

    Open Access Government   33   210 - 211   2022.1

  • テンソル分解を用いた教師なし学習による変数選択法を用いた薬物組織全体のモデル動物実験における薬物治療反応の普遍性 International coauthorship

    田口善弘, ターキー ターキー

    情報処理学会研究報告   2021-BIO-68 ( 3 )   1 - 7   2021.11

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  • 「編集委員に聞く|ncRNA・Cells誌」田口 善弘先生|中央大学 Invited

    田口 善弘

    MDPI ブログ   2021.11

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  • テンソル分解を用いた遺伝子発現からの神経疾患の創薬

    Y-h. Taguchi, Turki Turki

    情報処理学会研究報告   2021-BIO-67 ( 7 )   1 - 6   2021.9

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  • In Silico Drug Discovery for COVID-19 Using an Unsupervised Feature Extraction Method International journal

    Y-h. Taguchi

    Scientia   2021.9

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    DOI: 10.33548/SCIENTIA727

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  • 〈あなたを解雇します〉人生を予測される前に知っておくべきこと Invited

    田口善弘

    講談社ブルーバックス広報サイト   2021.7

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  • 〈あなたの心、読みます〉思想統制も可能!? 思考が読み取られる日はすぐそこ Invited

    田口善弘

    講談社ブルーバックス広報サイト   2021.7

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  • 〈あなたの顔、替えます〉リアル動画の改変技術、ディープフェイクでここまで可能に Invited

    田口善弘

    講談社ブルーバックス広報サイト   2021.7

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  • 〈あなたを完コピします〉高精度予測AIに、サイエンティストが追われるとき Invited

    田口善弘

    講談社ブルーバックス広報サイト   2021.7

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  • PCAUFEを用いたCOVID-19と他の肺疾患を区別する遺伝子の特定

    志茂 衛, 藤澤 孝太, 田口 善弘, 池松 真也, 宮田 龍太

    情報処理学会研究報告   2021-BIO-66 ( 30 )   1 - 2   2021.6

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  • シングルセル遺伝子発現プロファイル解析へのテンソル分解を用いた教師なし学習による変数選択法の応用

    田口 善弘, ターキー ターキー

    情報処理学会研究報告   2021-BIO-66 ( 25 )   1 - 5   2021.6

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  • Estimation of Metabolic Effects of Cadmium Exposure during Pregnancy by Tensor Decomposition

    Yuki Amakura, Y-H Taguchi

    IPSJ SIG technical reports   2021-BIO-66 ( 26 )   1 - 5   2021.6

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  • Identification of Enhancers and Promoters in the Genome by Multi-Dimensional Scaling

    Ryo Ishibashi, Y-H Taguchi

    IPSJ SIG technical reports   2021-BIO-66 ( 27 )   1 - 4   2021.6

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  • 国際会議参加報告「EUSTM2020」 Invited

    田口善弘

    日本バイオインフォマティクス学会 ニュースレター   39   17   2021.3

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  • Is Convex Dose Dependence A Side Effect That Multiple Drug Treatment Causes? Invited

    Y-h. Taguchi, Turki Turki

    Pharma Focus Asia   2021.3

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    Other Link: https://industry.pharmafocusasia.com/e-newsletters/1616757413-Mar-21-pfa-e-newsletter.html

  • カーネルテンソル分解を用いた教師なし学習による変数選択法 ~ バイオインフォマティクスへの応用 ~

    田口善弘

    情報処理学会研究報告   2021-BIO-65 ( 2 )   1 - 9   2021.3

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  • カーネルテンソル分解を用いた教師なし学習による変数選択法 ~ バイオインフォマティクスへの応用 ~

    田口善弘

    信学技法   120 ( 395 )   16 - 23   2021.3

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  • テンソル分解を用いた教師無し学習による変数選択法によるmiRNA/mRNA/プロテオームの統合解析

    田口善弘

    情報処理学会研究報告   2020-BIO-64 ( 10 )   1 - 5   2020.12

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  • 対面かオンラインか、それが問題だ Reviewed

    田口善弘

    大学の物理教育   26 ( 3 )   115 - 115   2020.11

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    Authorship:Lead author   Language:Japanese   Publishing type:Rapid communication, short report, research note, etc. (scientific journal)   Publisher:日本物理学会  

    DOI: 10.11316/peu.26.3_115

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  • 「生命はデジタルでできている」。 その衝撃的な可能性とは? Invited

    田口善弘

    Sustainable Business   2020.10

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  • Unsupervised feature extraction applied to bioinformatics

    Y-h. Taguchi

    Research Outreach   ( 115 )   154 - 157   2020.7

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    DOI: 10.32907/ro-115-154157

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  • Conjecturing human genes that are easy to be double strand breaks by tensor decomposition

    Nobuo Hosaka, Y-h. Taguchi

    IPSJ Technical Reports   2020-BIO-62 ( 2 )   1 - 6   2020.6

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  • テンソル分解に基づく教師なし学習による変数選択のRNAi 処理を行ったプラナリアのRNA-seq解析への適用

    田口善弘, 鹿島誠

    情報処理学会研究報告   2020-BIO-62 ( 1 )   1 - 4   2020.6

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  • 「生命はデジタルでできている!」のはご存じか? Invited

    田口善弘

    講談社ブルーバックス広報サイト   2020.6

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  • 生命はデジタルでできている Invited

    田口善弘

    本   45 ( 6 )   46 - 47   2020.6

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  • 新型コロナウイルスと「デジタルで闘う」科学者たち Invited

    田口善弘

    講談社ブルーバックス広報サイト   2020.5

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  • 日本企業はもっと博士号取得者を採用して、先端的な知識や専門外の知見を活用すべき Invited

    田口善弘

    epiST博士のキャリアstories   2020.5

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  • 物理学者、機械学習を使う Invited

    田口善弘

    数理科学   58 ( 5 )   63 - 63   2020.4

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  • 米国科学・工学・医学アカデミーによる量子コンピュータの進歩と展望」

    田口善弘

    大学の物理教育   26 ( 1 )   27 - 28   2020.3

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    DOI: 10.11316/peu.26.1_27

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  • Sp1を標的としたHBVウィルスの新規阻害剤の探索

    早川 路代, 梅山 秀明, 岩舘 満雄, 田口 善弘, 村上 善基

    情報処理学会研究報告   2020-BIO-61 ( 1 )   1 - 7   2020.3

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  • 機械学習による遺伝子情報の解析技術と 医薬品開発への応用 Invited

    田口 善弘

    ファームステージ   19 ( 11 )   15 - 20   2020.2

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  • Application of tensor decomposition based unsupervised feature extraction to single cell RNA-seq analysis

    Y-h. TAGUCHI

    IEICE Technical Report   119 ( 360 )   55 - 59   2020.1

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  • Society and Ethics in bioinformatics Invited

    Y-h. TAGUCHI

    IEICE Tech. Rep.   119 ( 329 )   47 - 50   2019.12

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  • テンソル分解を用いた教師なし学習による変数選択法のTCGAデータベースにおける卵巣がんデータのmicroRNA発現プロファイルとメチル化プロファイルへの適用

    田口 善弘, 呉 家樂

    情報処理学会研究報告   2019-BIO-60 ( 2 )   1 - 7   2019.12

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  • インストールいらずの LATEX 入門

    田口 善弘

    大学の物理教育   25 ( 3 )   147 - 148   2019.11

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    DOI: 10.11316/peu.25.3_147

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  • テンソル分解に基づく教師なし学習による変数選択はMicroRNAトランスフェクションにより仲介されるmRNAの配列非特異的オフターゲット調節の普遍的性質を同定することができる

    田口 善弘

    情報処理学会研究会報告   2019-BIO-59 ( 3 )   1 - 7   2019.9

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  • ラ・トッカータ:機械学習のコモディティ化 Invited Reviewed

    田口 善弘

    日本物理学会誌   74 ( 9 )   659 - 660   2019.9

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    DOI: 10.11316/butsuri.74.9_659

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  • 予備校のノリで学ぶ大学数学

    田口 善弘

    大学の物理教育   25 ( 2 )   103 - 104   2019.7

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    DOI: 10.11316/peu.25.2_103

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  • テンソル分解と主成分分解を用いた教師なし学習による社会性昆虫のカーストの脳の遺伝子発現プロファイルとDNAメチル化の解析

    田口 善弘

    情報処理学会研究報告   2019-BIO-58 ( 61 )   1 - 6   2019.6

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  • How well can serum miRNAs diagnose amyotrophic lateral sclerosis patients? Invited

    Y-h. Taguchi, Hsiuying Wang

    Atlas of Science   2019.6

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  • 日本たばこ産業 コンサルティング等業務委託費

    田口善弘

    2019.4

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  • 生物学と物理学

    田口 善弘

    窮理   12   1 - 9   2019.4

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  • 主成分分析を用いた教師無し学習による変数選択の一細胞RNA-seqへの応用

    田口 善弘

    情報処理学会研究報告   2019-BIO-57 ( 6 )   1 - 6   2019.3

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  • 筋萎縮性側索硬化症のためのマイクロRNAバイオマーカーの探索

    田口 善弘, 王 秀瑛

    情報処理学会研究報告   2018-BIO-56 ( 2 )   1 - 6   2018.12

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  • AI=機械学習とは何か(Ⅲ)

    田口 善弘

    rad-it21   2018.11

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  • Application of Tensor Decomposition Based Unsupervised Feature Extraction to Bioinformatics

    Y-h. Taguchi

    IEICE Technical Report   118 ( 284 )   345 - 352   2018.10

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  • AI=機械学習とは何か(I)

    田口 善弘

    rad-it21   2018.10

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  • AI=機械学習とは何か(Ⅱ)

    田口 善弘

    rad-it21   2018.10

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  • 疾患とDrugMatrixデータセットとの間の遺伝子発現の統合解析におけるテンソル分解を用いた教師無し学習による変数選択を用いた候補薬剤の同定

    田口善弘

    情報処理学会研究報告   2018-BIO-55 ( 1 )   1 - 6   2018.9

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  • テンソル分解を用いた教師なし学習による変数選択法のマルチビューデータ解析への応用

    田口善弘

    情報処理学会研究報告   2018-BIO-54 ( 37 )   1 - 6   2018.6

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  • AI+ゲノム科学=? ゲノム科学で起きること

    田口善弘

    AINOW   2017.12

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  • テンソル分解を用いた教師なし学習による心的外傷後ストレス障害由来の心臓病原因遺伝子の同定

    田口善弘

    情報処理学会研究報告   2017-BIO-51 ( 1 )   1 - 8   2017.9

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  • 主成分分析を用いた教師なし学習による変数選択のデング出血熱患者血液遺伝子発現プロファイル解析への応用 (情報論的学習理論と機械学習)

    田口 善弘

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   117 ( 110 )   119 - 124   2017.6

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  • 主成分分析を用いた教師なし学習による変数選択のデング出血熱患者血液遺伝子発現プロファイル解析への応用

    田口善弘

    情報処理学会研究報告   2017-BIO-50 ( 35 )   1 - 6   2017.6

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  • ピンチをチャンスに

    田口善弘

    自然科学書協会会報   ( 84 )   1 - 2   2017.4

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  • 主成分分析を用いた教師なし学習による変数選択を用いたヒストン脱アセチル化酵素阻害剤の機能探索

    田口善弘

    情報処理学会研究報告   2017-BIO-49 ( 1 )   1 - 6   2017.3

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  • 中央⼤学共同研究プロジェクト「FAMS を⽤いたタンパク質機能予測に基づくDrugDiscovery」研究報告

    田口善弘, 岩舘満雄, 梅山秀明, 内古閑伸之, マイケル グロミハ

    中央大学理工学研究所論文集   ( 22 )   33 - 37   2017.3

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  • マルチオミックスデータ解析による非小細胞肺がん遺伝子の推定

    田口善弘

    別冊 BIO Clinica 慢性炎症と疾患   6 ( 1 )   126 - 129   2017.1

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  • miRNA-mRNA相互作用同定を用いた腎芽腫関連遺伝子の推定

    田口善弘

    情報処理学会研究報告   2016-BIO-48 ( 1 )   1 - 6   2016.12

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  • Principal Component Analysis based unsupervised Feature Extraction applied to Bioinformatics

    116 ( 300 )   17 - 24   2016.11

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  • 主成分分析を用いた教師なし学習による変数選択法のバイオインフォマティクスへの応用

    田口善弘

    信学技報   116 ( 300 )   17 - 24   2016.11

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  • 主成分分析を用いた教師なし学習による出芽酵母の時間周期遺伝子発現プロファイルの解析

    田口善弘

    情報処理学会研究報告   2016-BIO-47 ( 5 )   1 - 6   2016.9

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  • 主成分分析を用いた教師なし学習による変数選択法を用いたがんにおけるmRNA-miRNA相互作用のより信頼性のある同定 (情報論的学習理論と機械学習)

    田口 善弘

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   116 ( 121 )   159 - 164   2016.7

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  • 主成分分析を用いた教師なし学習による変数選択法を用いたがんにおけるmRNA-miRNA相互作用のより信頼性のある同定 (ニューロコンピューティング)

    田口 善弘

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   116 ( 120 )   153 - 158   2016.7

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  • マルチオミックスデータ解析による非小細胞肺がん遺伝子の推定

    田口善弘

    メディカル・サイエンス・ダイジェスト   42 ( 9 )   394 - 397   2016.7

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  • 主成分分析を用いた教師なし学習による変数選択法を用いたがんにおけるmRNA-miRNA相互作用のより信頼性のある同定

    田口善弘

    情報処理学会研究報告   2016-BIO-46 ( 27 )   1 - 6   2016.7

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  • 違うとはどういうことか

    田口善弘

    数理科学   54 ( 5 )   56 - 57   2016.5

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  • 非小細胞肺がんのエピジェネティック療法標的遺伝子の推定

    田口善弘, 岩舘満雄, 梅山 秀明

    情報処理学会研究報告   2016-BIO-45 ( 6 )   1 - 7   2016.3

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  • Heuristic principal component analysis-based unsupervised feature extraction applied to gene expression analysis of amyotrophic lateral sclerosis data sets

    Y-H. Taguchi, Mitsuo Iwadate, Hideaki Umeyama

    IPSJ SIG Technical Report   2015-BIO-43 ( 6 )   1 - 6   2015.9

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  • Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease (情報論的学習理論と機械学習)

    Taguchi Y-h., Iwadate Mitsuo, Umeyama Hideaki

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   115 ( 112 )   1 - 8   2015.6

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  • Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease

    Y-h. Taguchi, Mitsuo Iwadate, Hideaki Umeyama

    IEICE Technical Report   115 ( 112 )   1 - 8   2015.6

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  • In silico spleen tyrosine kinase inhibitor screening by chooseLD

    Hideaki Umeyama, Mitsuo Iwadate, Y-h. Taguchi

    IPSJ SIG technical reports   2015-BIO-41 ( 9 )   1 - 6   2015.3

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  • Comparison of Hepatocellular Carcinoma miRNA Expression Profiling as Evaluated by Next Generation Sequencing and Microarray

    IPSJ SIG technical reports   2014 ( 2 )   1 - 5   2014.12

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    Although microarray has been an important tool that can perform extensive gene expression analyses, next generation sequencing (NGS) has recently arisen as an alternative methodology that can measure gene expression. In this paper, we have compared microarray and NGS quantitatively using microRNA measurements in hepatocellular carcinoma (HCC) and found that these two are coincident with each other. NGS also turned out to be used for biomarker between HCC and normal tissue if the recently proposed principal component analysis based unsupervised feature extraction was applied.

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  • Heuristic principal component analysis based unsupervised feature extraction and its application to bioinformatics

    YH Taguchi

    IEICE Technical Report   114 ( 306 )   87 - 94   2014.11

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  • Apparent microRNA-target-specific histone modification in mammalian spermatogenesis

    YH Taguchi

    IPSJ SIG technical reports   2014-BIO-39 ( 2 )   1 - 2   2014.9

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  • TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasis in non-small cell lung cancer

    Hideaki Umeyama, Mitsuo Iwadate, Y-h. Taguchi

    IPSJ SIG technical reports   2014 ( 8 )   1 - 6   2014.6

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    Background: Non-small cell lung cancer (NSCLC) remains lethal despite the development of numerous drug therapy technologies. About 85% to 90% of lung cancers are NSCLC and the 5-year survival rate is at best still below 50%. Thus, it is important to find drug target genes for NSCLC to develop an effective therapy for NSCLC. Results: Integrated analysis of publically available gene expression and promoter methylation patterns of two highly aggressive NSCLC cell lines generated by in vivo selection was performed. We selected eleven critical genes that may mediate metastasis using recently proposed principal component analysis based unsupervised feature extraction. The eleven selected genes were significantly related to cancer diagnosis. The tertiary protein structure of the selected genes were inferred by Full Automatic Modeling System, a profile based protein structure inference software, to determine protein functions and to specify genes that could be potential drug targets. Conclusions: We identified eleven potentially critical genes that may mediate NSCLC metastasis using bioinformatic analysis of publically available data sets. These genes are potential target genes for therapy of NSCLC. Among the eleven genes, TINAGL1 and B3GALNT1 are possible candidates for drug compounds that inhibit their gene expression.

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  • Subtype specific promoter methylation in glioblastoma

    Kiyohiko Sakamoto, YH Taguchi

    IPSJ SIG technical reports   1 - 2   2014.6

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  • Aberrant expression of microRNA according to aging is participating in hepatocarcinogenesis

    Yoshiki Murakami, Saori Itami, Hidenori Toyoda, Takashi Kumada, Toshihito Tanahashi, Etsushi Kawamura, Atsushi Hagihara, Sawako K. Uchida, Hiroyasu Morikawa, Masaru Enomoto, Akihiro Tamori, Norifumi Kawada, Y-H Taguchi

    HEPATOLOGY   60   881A - 882A   2014

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  • Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as candidate drug targets

    Ryoichi Kinoshita, Mitsuo Iwadate, Hideaki Umeyama, Y-H. Taguchi

    IPSJ SIG technical reports   2013 ( 23 )   1 - 6   2013.12

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    Background: Aberrant DNA methylation is often associated with cancers. Thus, screening genes with cancer-associated aberrant DNA methylation is a useful method to identify candidate cancer-causing genes. Aberrant DNA methylation is also genotype dependent. Thus, the selection of genes with genotype-specific aberrant DNA methylation in cancers is potentially important for tailor-made medicine. The selected genes are important candidate drug targets. Results: The recently proposed principal component analysis based selection of genes with aberrant DNA methylation was applied to genotype and DNA methylation patterns in squamous cell carcinoma measured using single nucleotide polymorphism (SNP) arrays. SNPs that are frequently found in cancers are usually aberrantly methylated, and the genes that were selected using this method were reported previously to be related to cancers. Thus, genes with genotype-specific DNA methylation patterns will be good therapeutic candidates. The tertiary structures of the proteins encoded by the selected genes were successfully inferred using two profile-based protein structure servers, FAMS and Phyre2. Candidate drugs for three of these proteins, tyrosine kinase receptor (ALK), EGLN3 protein, and NUAK family SNF1-like kinase 1 (NUAK1), were identified by ChooseLD. Conclusions: We detected genes with genotype-specific DNA methylation in squamous cell carcinoma that are candidate drug targets. Using in silico drug discovery, we successfully identified several candidate drugs for the ALK, EGLN3 and NUAK1 genes that displayed genotype-specific DNA methylation.

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  • Protein Subcellular Location Prediction Using Principal Component Analysis

    Daichi Nogami, Yuuichi Nakano, Y-H. Taguchi

    IPSJ SIG technical reports   2013-BIO-34 ( 28 )   1 - 2   2013.6

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  • Discrimination of symbiotic/parasitic bacterial type III secretion system effector protein using principal component analysis

    Yuuichi Nakano, Mitsuo Iwadate, Hideaki Umeyama, Y-H. Taguchi

    IPSJ SIG technical reports   2013 ( 10 )   1 - 8   2013.6

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  • Distinction of Type 2 diabetes using PCA, miRNA as features

    Shodai Katsukawa, Y-H. Taguchi

    IPSJ SIG technical reports   1 - 2   2013.6

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  • Elucidating soil microbial communities in agricultural soils

    Yudai Suzuki, Kazunari Yokoyama, Naomi Sakuramoto, Y-H. Taguchi

    IPSJ SIG technical reports   113 ( 111 )   1 - 2   2013.6

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    Soil microbiological biodiversity has recently been shown to be causally related to soil diseases. In this study, we attempted to elucidate soil microbiological interactions by numerically analyzing the carbon-resource consumption rates in soils provided by a number of domestic companies and research institutes.

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  • Possible miRNA coreguation of target genes in brain regions by both differential miRNA expression and miRNA-targeting-specific promoter methylation

    Y-h. Taguchi

    IPSJ SIG technical reports   2013-BIO-33 ( 6 )   1 - 6   2013.3

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  • エクソソーム中マイクロRNAを利用した慢性肝疾患診断法の開発

    村上善基, 河田則文, 棚橋俊仁, 田口善弘

    肝胆膵   67 ( 1 )   87 - 92   2013.1

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  • 肝疾患におけるmicroRNA診断

    村上善基, 棚橋俊仁, 田口善弘

    細胞工学   23 ( 1 )   79 - 84   2013.1

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  • The Diagnostic Method of Chronic Liver Disease Using microRNA Expression Pattern

    32 ( 1 )   79 - 84   2013

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  • Comprehensive miRNA expression analysis in peripheral blood can diagnose liver disease

    Yoshiki Murakami, Hidenori Toyoda, Toshihito Tanahashi, Junko Tanaka, Takashi Kumada, Yusuke Yoshioka, Nobuyoshi Kosaka, Takahiro Ochiya, Y-h. Taguchi

    IPSJ SIG technical reports   2012-BIO-32 ( 12 )   1 - 6   2012.12

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  • 情報学的スクリーニングによる自己免疫疾患遺伝子とそのFAMSを用いた複合体モデリングおよびインシリコスクリーニング

    岩舘満雄, 田口善弘, 梅山秀明

    構造活性相関シンポジウム講演要旨集   40   50 - 51   2012.11

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  • Competitive target gene regulation by promoter methylation and miRNA

    Y-h. Taguchi

    IPSJ SIG technical reports   2012-BIO-31 ( 1 )   1 - 6   2012.10

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  • 肝疾患におけるmiRNA診断

    村上善基, 田中正視, 棚橋俊仁, 田口善弘

    臨床・創薬利用が見えてきたmicroRNA   2012.9

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  • Analysis of single cell gene expression of mESC and MEF by NGS

    Kohei Iijima, Y-h. Taguchi

    IPSJ SIG technical reports   2012-BIO-29 ( 18 )   1 - 2   2012.6

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  • Structure prediction with FAMS for proteins screened critically to autoimmune diseases based upon bioimformatics

    Shigeharu Ishida, Hideaki Umeyama, Mitsuo Iwadate, Y-h.Taguchi

    IPSJ SIG technical reports   2012-BIO-29 ( 12 )   1 - 6   2012.6

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  • Individual difference of promoter methylation patterns in human brain tissue

    Miyuki Owada, Y-h. Taguchi

    IPSJ SIG technical reports   2012-BIO-29 ( 17 )   1 - 2   2012.6

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  • Principal component analysis for bacterial proteomic analysis II

    Y-h. Taguchi, Akira Okamoto

    IPSJ SIG Technical Report   2012-BIO-28 ( 16 )   1 - 6   2012.3

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    Proteomic analysis is very useful procedure to understand the bacterial behavior changes with reaction to the external environment. This is because most of genomic information of bacteria is devoted to code enzyme to control metabolic networks inside the individual cell. In this paper, we have performed proteomic analysis of Streptococcus pyogenes, which is known to be a flesh-eating bacteria and can cause several human life-threatening diseases. Its proteome during growth phase is measured for four time points under two different incubation conditions; with and without shaking. The purpose of it is to understand adaptivity to oxidative stress. Principal component analysis is applied and turns out to be useful to depict biologically important proteins for both supernatant and cell components.

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  • Inference of target gene regulation via miRNAsduring cell senescence by MiRaGE Server

    Y-h. Taguchi

    IPSJ SIG Technical Report   2012-BIO-28 ( 3 )   1 - 6   2012.3

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    MiRNAs are recently known to be critical players causing cell senescence, by regulating target genes. Thus inference of miRNAs critically regulating target genes is important. However, miRNAs critically regulating target genes are believed to have significant fold changes, typically upregulations, during cell senescence. In this study, we consider the target gene regulation by miRNAs together with miRNAs expression change during fibroblast IMR90 cell senescence. Then we found that the simultaneous consideration of two criterion lists more feasible miRNAs: i.e., miRNAs being more often reported to be down/upregulated and/or having biological backgrounds inducing cell senescence. Thus, the amount of target gene regulation, which can be inferred by the recently developed MiRaGE Server, is recommended to be considered together for the estimation of miRNAs critically contributing to cell senescence.

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  • Disease suppressive soil has both diverse and uniform ecology: Modeling and characterization from the viewpoint of microbiology and biodiversity

    Y-h. Taguchi, Kazunari Yokoyama

    IPSJ SIG Technical Report   2012-BIO-28 ( 10 )   1 - 6   2012.3

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    Soil disease suppression is a worldwide important issue in order to realize stable food supply to people. In spite of that, no established indicators of soil disease suppression have been found out yet. This prevents us from controlling well soil state such that no diseases take place. In this paper, we have proposed a new biological indicator of soil disease suppression; the ability of bacteria to consume carbon resources, which can be automatically observed by Omunilog ID system during a duration of one or two days. This indicator turned out to distinguish disease suppressive soils from others. We have modeled these characteristic time developments of consumption of carbon resources by the simple ecological model where bacteria compete with each other for carbon resources. Measured ecological structure of soil bacteria can fit with the theoretical prediction well. In order to find characteristic features for each of soils, observed time developments are embedded into two dimensional space by non-metric multidimensional method. It results in the almost one dimensional arrangements of embedded points. By analyzing spacial distributions of each carbon resources in the embedded space, healthy soil turns out to have mostly uniform distribution along this one dimensional arrangement. Since sick soil and non-soil example have rather localized distributions, the ecological systems in more disease suppressive soil are both more diverse and more uniform. Since this indicator can be extremely easily and quickly obtained automatically, it is expected to use in order to validate many efforts to try to improve soil before any harvests very much.

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  • Refined blood-borne miRNome of human diseases via PCA-based feature extraction

    Y-h. Taguchi, Yoshiki Murakami

    IPSJ SIG Technical Report   2012-BIO-28 ( 13 )   1 - 6   2012.3

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    Disease biomarker using blood is clinically important, since blood is easy to obtain from patients, thus it requires relatively less stress. However, blood generally reflects not only targeted diseases but also whole body status of patients. Thus, it is important which contents of blood are considered. Recently, miRNAs in blood, blood-borne miRNome, turns out to be promising candidates for blood based biomarker for diseases. In this paper, we propose a new method based upon principal component analysis to identify better candidates for miRNAs as blood based biomarker using miRNA expression profiles of patients. Our method based upon principal components analysis provides us better blood-borne miRNome to discriminate diseases from healthy controls. They are hsa-miR-425,hsa-miR-15b,hsa-miR-185, hsa-miR-92a, hsa-miR-140-3p, hsamiR-320a, hsa-miR-486-5p, hsa-miR-16, hsa-miR-191, hsa-miR-106b, hsa-miR-19b, and hsa-miR-30d and are previously extensively reported to be cancer/disease related miRNAs. We have found that these common miRNAs are expressive or suppressive significantly in most of diseases/cancers, but in diseases/cancers specific combinatory manner. It enables us to discriminate cancers/diseases from healthy control well.

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  • 主成分分析によるバクテリアのプロテオーム解析

    岡本陽, 田口善弘

    情報処理学会研究報告   SIG-BIO-26 ( 6 )   1 - 6   2011.9

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  • Principal Component Analysis for Bacterial Proteomic Analysis

    Akira Okamoto, Y-h. Taguchi

    IPSJ SIG Technical Report   1 - 6   2011.9

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  • Search of miRNAs critical for medulloblastoma formation using MiRaGE method

    TAGUCHI Y-H., YASUDA JUN

    IEICE technical report   111 ( 96 )   21 - 26   2011.6

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    MiRaGE method estimates critical miRNAs based upon gene expression profile of their target genes. Expression profile of mRNA/miRNA is measured for tissues from neonatal and adult mice (6 days and 30 days after birth: P6 and P30, respectively), and medulloblastomas (2-3 months old: MB) with Agilent microarray is analyzed by MiRaGE method. Comparison between P30 and MB gives us the list of significantly up(down)regulated miRNAs whose target genes are down(up)regulated. The obtained list is biologically reasonable, possibly due to accuracy of Agilent microarray measurement, thus we conclude that MiRaGE method is useful to investigate tissue formation processes, too.

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  • Protein binding prediction using non-metric multidimensional scaling method

    Hiromu Mogi, Y-H. Taguchi

    IPSJ SIG Technical Report   2011-BIO-25 ( 41 )   1 - 2   2011.6

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  • Gene expression regulation during differentiation from murine ES cells due to microRNA

    Masato Yoshizawa, Y-H. Taguchi

    IPSJ SIG Technical Report   1 - 2   2011.6

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  • Search of miRNAs critical for medulloblastoma formation using MiRaGE method

    Y-h. Taguchi, Jun Yasuda

    IPSJ SIG Technical Report   2011-BIO-25 ( 5 )   1 - 6   2011.6

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  • Feature extraction for discriminance of symbiotic/parasitic bacterial type III effector protein using principal component analysis

    Yuichi Nakano, Y-h. Taguchi

    IPSJ SIG Technical Report   2011-BIO-25 ( 37 )   1 - 2   2011.6

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  • Tissue specific methylation and genotype

    Ryoichi Kinoshita, Y-H. Taguchi

    IPSJ SIG Technical Report   2011-BIO-25 ( 39 )   1 - 2   2011.6

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  • The Analysis of DNA Shuffling by nMDS

    Ryota Doi, Y-H. Taguchi

    IPSJ SIG Technical Report   2011-BIO-25 ( 40 )   1 - 2   2011.6

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  • Gene expression regulation during differenctiation from murine ES cells due to microRNA

    Masato Yoshizawa, Yoshihiro Taguchi

    2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS   2011-BIO-25 ( 38 )   948 - 949   2011

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    we have infer gene expression regulation via microRNA using both gene expression profile and target gene tables during the differentiation from embryonic stem cell to neuronal cells. Comparison between obtained list of microRNA which regulates gene expression significantly and that of miRNAs whose expression level changes significantly results in significant overlap.

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  • Inference of microRNA transfection via target gene expression (ニューロコンピューティング)

    Taguchi Y-h., Yasuda Jun

    電子情報通信学会技術研究報告   110 ( 83 )   31 - 36   2010.6

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  • Inference of microRNA transfection via target gene expression

    Y-h. Taguchi, Jun Yasuda

    IPSJ SIG Technical Report   2010-BIO-21 ( 6 )   1 - 6   2010.6

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  • Gene Expression Regulation via Pluripotency Related Transcription Factors and Micro RNA in Mouse Embryonic Stem Cell during Differentiation

    Y-h. Taguchi

    The 3rd Pan Pacific Symposium on Stem Cells Research   2010.4

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  • Gene Expression Regulation via Pluripotency Related Transcription Factors and Micro RNA in Mouse Embryonic Stem Cell during Differentiation

    Y-h. Taguchi

    The 3rd Pan Pacific Symposium on Stem Cells Research   2010.4

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  • PCAを用いた2群の有意差検定

    田口善弘

    人工知能学会研究会資料 データマイニングと統計数理研究会(第 12 回)   SIG-DMSM-A903   38 - 47   2010.3

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  • Analysis of protein expression during honey bee larval development via non-metric multidimensional scaling method

    Y-h. Taguchi

    IPSJ SIG Technical Reports 2008-BIO-16   2009 ( 25 )   17 - 20   2009.3

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  • 階層構造の科学

    田口善弘

    大学の物理教育   2008 ( 3 )   154 - 155   2008.11

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  • 研究会報告「非線型科学と統計科学の対話」

    伊庭幸人, 末谷大道, 青柳富誌生, 田口善弘, 小松崎民樹

    物性研究   91 ( 2 )   113 - 174   2008.11

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  • 非計量多次元尺度構成法とその生体生命情報解析への応用

    田口善弘

    物性研究   91 ( 2 )   137 - 143   2008.11

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    非計量多次元尺度構成法は、もともと社会科学で使われていた方法であるが、我々はこの10年ほどの間、生体生命情報(いわゆるバイオインフォマティクス)への応用を試みてきた。今回はその中から、分裂酵母の細胞分裂周期のマイクロアレイ実験の解析結果を報告する。

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  • A Novel Discrimination of GO term Annotated Proteins based on Amino Acid Occurrence and Composition

    Y-h. Taguchi, M. Michael Gromiha

    Pattern Recognition in Bioinformatics, Thirs IAPR Internatiocal Workshop, PRIB2008, Supplementary Proccedings,   2008.10

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  • Markov random field models for protein function prediction

    Hisamitsu Akiba, Y-h. Taguchi

    情報処理学会研究報告   2008 ( 86 )   51 - 54   2008.9

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  • Markov random field models for protein function prediction

    Hisamitsu Akiba, Y-h. Taguchi

    IPSJ SIG Technical Reports 2008-BIO-14   51 - 54   2008.9

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  • Cancer discrimination based upon non-metric multidimensional scaling method

    KONNO Masaru, TAGUCHI Y-h.

    IPSJ SIG technical reports   2008 ( 58 )   13 - 16   2008.6

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    It is an important task to discrminate cancer using microarray experiments. Usually, since the number of samples is smaller than that of genes, it is unavoidable to select a part of genes for discrimination in order to avoid over fitting, but this choice may differ from samples to samples. In this paper, we propose two new methods for discrimination without selecting genes using non-metric multidimensional scaling (nMDS). One is the discrimination based upon embedding by employing Euclidean distance between gene expression profiles as dissimilarity and another is based upon iterative embedding after every discrimination. Both two achieve competitive performance with the conventional voting method.

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  • Cancer discrimination based upon non-metric multidimensional scaling method

    Masaru Konno, Y-h. Taguchi

    IPSJ SIG Technical Reports 2008-BIO-13   2008 ( 58 )   13 - 16   2008.6

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    It is an important task to discrminate cancer using microarray experiments. Usually, since the number of samples is smaller than that of genes, it is unavoidable to select a part of genes for discrimination in order to avoid over fitting, but this choice may differ from samples to samples. In this paper, we propose two new methods for discrimination without selecting genes using non-metric multidimensional scaling (nMDS). One is the discrimination based upon embedding by employing Euclidean distance between gene expression profiles as dissimilarity and another is based upon iterative embedding after every discrimination. Both two achieve competitive performance with the conventional voting method.

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  • Analysis of DNA copy number alterations with non-metric multidimensional scaling method

    Fumiaki Kataoka, Y-h. Taguchi

    情報処理学会研究報告   2008 ( 15 )   55 - 61   2008.3

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  • Analysis of DNA copy number alterations with non-metric multidimensional scaling method

    Fumiaki Kataoka, Y-h. Taguchi

    IPSJ SIG Technical Reports 2008-BIO-12   55 - 61   2008.3

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  • 読書アンケート

    田口善弘

    みすず   50 ( 1 )   p.76   2008.2

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  • フロ桶はいついっぱいになる?

    田口善弘

    数学セミナー   46 ( 9 )   44 - 47   2007.9

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  • A robust measure of correlation between two genes on a microarray using non-metric multidimensional scaling method

    Y-h. Taguchi

    IPSJ SIG Technical Reports 2007-BIO-10   1 - 8   2007.9

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  • A robust measure of correlation between two genes on a microarray using non-metric multidimensional scaling method

    Y-h. Taguchi

    情報処理学会研究報告   2007 ( 89 )   1 - 8   2007.9

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  • Nonmetric distances for Barcode of Life

    Hisamitsu Akiba, Y-h. Taguchi

    情報処理学会研究報告   2007 ( 60 )   31 - 38   2007.6

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  • Nonmetric distances for Barcode of Life

    Hisamitsu Akiba, Y-h. Taguchi

    IPSJ SIG Technical Reports 2007-BIO-9   31 - 38   2007.6

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  • Comparison of amino acid occurrence and composition for predicting protein folds

    Y-h. Taguchi, M. Michael Gromiha

    情報処理学会研究報告   2007 ( 21 )   9 - 16   2007.3

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  • Comparison of amino acid occurrence and composition for predicting protein folds

    Y-h. Taguchi, M. Michael Gromiha

    IPSJ SIG Technical Reports 2006-BIO-8   9 - 16   2007.3

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  • 20pXJ-1 Analysis of my class questionnaire XI

    Taguchi Y-h.

    Meeting abstracts of the Physical Society of Japan   62 ( 1 )   386 - 386   2007.2

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  • 19aTC-12 Recognition of protein folds using amino acid occurrence

    Taguchi Y-h., Gromiha M. Michael

    Meeting abstracts of the Physical Society of Japan   62 ( 1 )   337 - 337   2007.2

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  • 18pTC-10 Gene expression profile analysis for circadian promoter activities of cyanobacterial bioluminescent reporter strains using non-metric multidimensional scaling

    Oyama Tokitaka, Itoh Hiroshi, Kondoh Takao, Taguchi Y-h.

    Meeting abstracts of the Physical Society of Japan   62 ( 1 )   331 - 331   2007.2

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  • 読書アンケート

    田口善弘

    みすず   49 ( 1 )   p.73   2007.2

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  • Gene arrangement for cell division cycle microarray experiments without sinusoidal fittings

    TAGUCHI Y-h.

    IPSJ SIG Notes   2006 ( 135 )   173 - 180   2006.12

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    Although it is important task to analyze time course gene expression profiles, it is very difficult to do this. In this paper, we have focused cell division cycle microarray experiments (synchronized by elurtiation) of S. pombe to figure out the above problems. After we have shown that conventional sinusoidal fittings have several problems when being applied to cell division cycle microarray experiments, we propose non-metric multidimensional scaling (nMDS) method for alternatives. The lesson that must be learnt is that less assumptions bring us more information, since nMDS requires the least assumptions to analyze gene expression data.

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  • Gene arrangement for cell division cycle microarray experiments without sinusoidal fittings

    Y-h. Taguchi

    IPSJ SIG Technical Reports 2006-BIO-7   173 - 180   2006.12

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  • 23pWA-12 Analysis of my class questionnaire IX

    Taguchi Y-h.

    Meeting abstracts of the Physical Society of Japan   61 ( 2 )   295 - 295   2006.8

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  • 24aRC-2 Detection of cell cycle regulated genes of S. pombe without sinusoidal fittings II

    Taguchi Y-h.

    Meeting abstracts of the Physical Society of Japan   61 ( 2 )   253 - 253   2006.8

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  • 自然界に現れるランダムネス

    田口善弘

    数理科学   44 ( 9 )   48 - 53   2006.8

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  • Bayesian-based gender recognition for face images

    Masahiro Chiba, Y-h. Taguchi

    IPSJ SIG Technical Reports 2006-HI-118   2006 ( 52 )   1 - 8   2006.5

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  • Bayesian-based gender recognition for face images

    Masahiro Chiba, Y-h. Taguchi

    IPSJ SIG Technical Reports 2006-HI-118   1 - 8   2006.5

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  • 27pTH-7 Detection of cell cycle regulated genes of S. pombe without sinusoidal fittings

    Taguchi Y-h.

    Meeting abstracts of the Physical Society of Japan   61 ( 1 )   353 - 353   2006.3

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  • 30pUA-15 The face detection using non metric multi demensional scaling

    Yamanaka M., Taguchi Y-h.

    Meeting abstracts of the Physical Society of Japan   61 ( 1 )   337 - 337   2006.3

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  • 28aZA-7 Analysis of my class questionnaire IX

    Taguchi Y-h.

    Meeting abstracts of the Physical Society of Japan   61 ( 1 )   404 - 404   2006.3

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  • 読書アンケート

    田口善弘

    みすず   48 ( 1 )   101 - 102   2006.2

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  • Noise reduction procedure for microarray experiments with non-metric multidimensional scaling method

    Y-h. Taguchi, Satwik Rajaram

    IPSJ SIG Technical Reports   2006 ( 13 )   9 - 16   2006.2

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    Although microarray experiments are widely used to investigate gene expression profiles, there are very few genes whose expressions levels are affected by the specific conditions being changed in an experiment. Thus, most gene expression changes are largely due to noise effects. The selection criterion for genes is often based on the level of gene expression. This may be inconsistent with the analysis which often uses correlation coefficients where the amplitude is ignored. We recently invented a new non-metric multidimensional scaling method (nMDS) algorithm and applied it to the analysis of gene expression profiles obtained from (say) microarray experiments. In this paper, we demonstrate that nMDS can be used as gene selection tool for gene expression profiles.

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  • Noise reduction procedure for microarray experiments with non-metric multidimensional scaling method

    TAGUCHI Y-h., RAJARAM Satwik

    IPSJ SIG technical reports   2006 ( 13 )   9 - 16   2006.2

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    Although microarray experiments are widely used to investigate gene expression profiles, there are very few genes whose expressions levels are affected by the specific conditions being changed in an experiment. Thus, most gene expression changes are largely due to noise effects. The selection criterion for genes is often based on the level of gene expression. This may be inconsistent with the analysis which often uses correlation coefficients where the amplitude is ignored. We recently invented a new non-metric multidimensional scaling method (nMDS) algorithm and applied it to the analysis of gene expression profiles obtained from (say) microarray experiments. In this paper, we demonstrate that nMDS can be used as gene selection tool for gene expression profiles.

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  • Detecting Cell Cycle Regulated Genes of Schizosaccharomyces pombeby using Nonmetric multidimensional scaling without sinusoidal fitting

    TAGUCHI Y-h.

    IPSJ SIG Technical Report   2005 ( 128 )   59 - 66   2005.12

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  • Detecting Cell Cycle Regulated genes in S. pombe with Non-metric Multidimensional Scaling without sinusoidal fittings

    Y-h. Taguchi

    IPSJ SIG Technical Reports   59 - 66   2005.12

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  • SYNC

    田口善弘

    日経サイエンス   35 ( 7 )   p.124   2005.7

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  • 物理学会員の研究環境は今? --- アンケート調査を中心に ---

    日本物理学会研究者環境分析委員会, 青木健一, 伊藤厚子, 伊藤伸泰, 主査, 延与桂子, 佐藤丈, 田口善弘, 登谷美穂子, 長谷正司, 坂東昌子, 委員長, 平野琢也, 古川はづき, 村

    日本物理学会誌   60 ( 6 )   466 - 470   2005.6

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  • 研究者環境分析委員会-分析結果報告-

    日本物理学会研究者環境分析委員会, 青木健一, 伊藤厚子, 伊藤伸泰, 主査, 延与桂子, 佐藤丈, 田口善弘, 登谷美穂子, 長谷正司, 坂東昌子, 委員長, 平野琢也, 古川はづき, 村

    2005.5

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  • 26pYW-8 A noise reduction method of gene expression experiments using non-metric multidimensonal scaling method

    Taguchi Y-h., Oono Y.

    Meeting abstracts of the Physical Society of Japan   60 ( 1 )   367 - 367   2005.3

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  • 連載 科学をよむ 量子,量子,量子!

    田口善弘

    数学セミナー   44 ( 3 )   82 - 83   2005.3

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  • NOISE REDUCTION PROCEDURE FOR GENE EXPRESSION DATA USING NON-METRIC MULTIDIMENSIONAL SCALING METHOD

    Y-h. Taguchi, Y. Oono

    ISM Report on Research and Education   294 - 295   2005.3

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  • NOISE REDUCTION PROCEDURE FOR GENE EXPRESSION DATA USING NON-METRIC MULTIDIMENSIONAL SCALING METHOD

    Y-h. Taguchi, Y. Oono

    ISM Report on Research and Education   21   294 - 295   2005.3

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  • 連載 科学をよむ シナプスなあなた

    田口善弘

    数学セミナー   44 ( 2 )   90 - 91   2005.2

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  • 連載 科学をよむ 人は生まれながらにして…

    田口善弘

    数学セミナー   44 ( 1 )   96 - 97   2005.1

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  • 連載 科学をよむ We are (not) alone.

    田口善弘

    数学セミナー   43 ( 12 )   88 - 89   2004.12

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  • 連載 科学をよむ セーガンが憂えた未来

    田口善弘

    数学セミナー   43 ( 11 )   98 - 99   2004.11

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  • 連載 科学をよむ 宇宙という真実

    田口善弘

    数学セミナー   43 ( 10 )   92 - 93   2004.10

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  • 連載 科学をよむ ワトソンvs.ドーキンス

    田口善弘

    数学セミナー   43 ( 9 )   92 - 93   2004.9

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  • 13aTC-11 A Model of Visual Intelligence with Neural Network

    Matsumura Koji, Taguch Y h

    Meeting abstracts of the Physical Society of Japan   59 ( 2 )   211 - 211   2004.8

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  • 14pTD-1 The Basic Image Transaction With Non Metric Multi Dimensional Scaling

    Yamanaka Masao, Taguchi Y-h

    Meeting abstracts of the Physical Society of Japan   59 ( 2 )   268 - 268   2004.8

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  • 14pTD-2 Statistical Feature Extraction from Images through Ordinal Correlations

    Tsukahara H, Taguchi Y-h, Oono Y

    Meeting abstracts of the Physical Society of Japan   59 ( 2 )   269 - 269   2004.8

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  • 連載 科学をよむ 創発のトンデモ度はいくつ?

    田口善弘

    数学セミナー   43 ( 8 )   96 - 97   2004.8

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  • 形の科学 百科事典

    田口善弘

    785 - 786   2004.8

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  • 連載 科学をよむ 生命と惑星と

    田口善弘

    数学セミナー   43 ( 7 )   94 - 95   2004.7

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  • 連載 科学をよむ 生命の未来

    田口善弘

    数学セミナー   43 ( 6 )   88 - 89   2004.6

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  • 連載 科学をよむ 異端の科学に生きて

    田口善弘

    数学セミナー   43 ( 5 )   96 - 97   2004.5

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  • 連載 科学をよむ DNAとメアリー・アニング

    田口善弘

    数学セミナー   43 ( 4 )   100 - 101   2004.4

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  • 28aYB-1 Analysis of Questionaire I : A derivation of activity index

    Taguchi Y-h

    Meeting abstracts of the Physical Society of Japan   59 ( 1 )   131 - 131   2004.3

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  • 28aYB-7 From the point of view of activity index

    Taguchi Y-h

    Meeting abstracts of the Physical Society of Japan   59 ( 1 )   133 - 133   2004.3

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  • 連載 科学をよむ たまには最先端を…

    田口善弘

    数学セミナー   43 ( 3 )   76 - 77   2004.3

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  • 歴史の方程式

    田口善弘

    日経サイエンス   34 ( 3 )   p.109   2004.3

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  • 連載 科学をよむ 人が死を見つめるとき

    田口善弘

    数学セミナー   43 ( 2 )   78 - 79   2004.2

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  • 連載 科学をよむ 感染症は終らない

    田口善弘

    数学セミナー   43 ( 1 )   82 - 83   2004.1

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  • 連載 科学をよむ 脳は心,心は脳

    田口善弘

    数学セミナー   42 ( 12 )   92 - 93   2003.12

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  • 連載 科学をよむ 時間をめぐって

    田口善弘

    数学セミナー   42 ( 11 )   86 - 87   2003.11

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  • 連載 科学をよむ 魔術とニュートン

    田口善弘

    数学セミナー   42 ( 10 )   82 - 83   2003.10

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  • 連載 科学をよむ 自然科学から離れて

    田口善弘

    数学セミナー   42 ( 9 )   80 - 81   2003.9

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  • A Toymodel of Flying Snake's Glide

    Matumura Koji, Taguchi Y-h.

    Meeting abstracts of the Physical Society of Japan   58 ( 2 )   241 - 241   2003.8

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  • 20pZC-10 Analysis of micorarray data of cell cycle regulated genes using nonmetric multidimensional sealing

    Taguchi Yh., Oono Y.

    Meeting abstracts of the Physical Society of Japan   58 ( 2 )   296 - 296   2003.8

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  • 20pZC-11 Analysis of Microarray Data of C. Elegans using Nonmetric Multidimencional scaling III

    Taguchi Y-h., Oono Y.

    Meeting abstracts of the Physical Society of Japan   58 ( 2 )   297 - 297   2003.8

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  • An analysis of a questionaire of a lecture : V

    Taguchi Y-h

    Meeting abstracts of the Physical Society of Japan   58 ( 2 )   326 - 326   2003.8

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  • 連載 科学をよむ 科学史の憂鬱?

    田口善弘

    数学セミナー   42 ( 8 )   92 - 93   2003.8

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  • 連載 科学をよむ 脳科学の現在

    田口善弘

    数学セミナー   42 ( 7 )   84 - 85   2003.7

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  • 連載 科学をよむ 片すみの科学書の愉しみ

    田口善弘

    数学セミナー   42 ( 6 )   78 - 79   2003.6

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  • 連載 科学をよむ 科学的に考えるということ

    田口善弘

    数学セミナー   42 ( 5 )   80 - 81   2003.5

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  • 連載 科学をよむ 病原菌は進化する?

    田口善弘

    数学セミナー   42 ( 4 )   82 - 83   2003.4

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  • 言語と進化

    田口善弘

    中央評論   55 ( 1 )   20 - 26   2003.4

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  • How does home work affect home work time?

    Taguchi Yh.

    Meeting abstracts of the Physical Society of Japan   58 ( 1 )   376 - 376   2003.3

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  • Gender Equality Bureau from a male point of views

    Taguchi Yh.

    Meeting abstracts of the Physical Society of Japan   58 ( 1 )   389 - 389   2003.3

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  • Analysis of microarray data of C. elegans with non-metric multidimencional scaling

    Taguchi Y.h., Oono Y

    Meeting abstracts of the Physical Society of Japan   58 ( 1 )   339 - 339   2003.3

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  • 29pWE-12 バルハン砂丘の形態・分布の数値計算モデル

    柴田 純, 田口 善弘, 西森 拓

    日本物理学会講演概要集   58 ( 1 )   286 - 286   2003.3

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  • 連載 科学をよむ 進化!進化!進化!(2)

    田口善弘

    数学セミナー   42 ( 3 )   92 - 93   2003.3

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  • ことし読むいち押しガイド 2003

    田口善弘

    203 - 205   2003.3

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  • 連載 科学をよむ 病は血から

    田口善弘

    数学セミナー   42 ( 2 )   88 - 89   2003.2

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  • 連載 科学をよむ 進化!進化!進化!(1)

    田口善弘

    数学セミナー   42 ( 1 )   82 - 83   2003.1

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  • 連載 科学をよむ 過去の歴史に学んで

    田口善弘

    数学セミナー   41 ( 12 )   86 - 87   2002.12

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  • 連載 科学をよむ 死すべき運命の……

    田口善弘

    数学セミナー   41 ( 11 )   88 - 89   2002.11

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  • 連載 科学をよむ いい科学書の条件とは?

    田口善弘

    数学セミナー   41 ( 10 )   86 - 87   2002.10

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  • 連載 科学をよむ 古生物学の愉しみ

    田口善弘

    数学セミナー   41 ( 9 )   90 - 91   2002.9

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  • 連載 科学をよむ なぜ,数学では天才がエライ?

    田口善弘

    数学セミナー   41 ( 8 )   86 - 87   2002.8

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  • 連載 科学をよむ 科学に革命起きるとき

    田口善弘

    数学セミナー   41 ( 7 )   96 - 97   2002.7

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  • ゼロから学ぶベクトル解析

    田口善弘

    大学の物理教育   2002 ( 2 )   2002.7

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  • 連載 科学をよむ 暗号はユダヤ教から

    田口善弘

    数学セミナー   41 ( 6 )   84 - 85   2002.6

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  • 連載 科学をよむ 人はなぜだまされるのか?

    田口善弘

    数学セミナー   41 ( 5 )   92 - 93   2002.5

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  • 連載 科学をよむ 事始め――数学脳は存在するか?

    田口善弘

    数学セミナー   41 ( 1 )   84 - 85   2002.4

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  • 異星人伝説

    田口善弘

    大学の物理教育   2002 ( 1 )   2002.3

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  • Analysis of segregation property of burden using 2-dimensional discrete model

    Shinroku Matsuzaki, Yoshihiro Taguchi

    Tetsu-To-Hagane/Journal of the Iron and Steel Institute of Japan   88 ( 12 )   823 - 830   2002

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    A segregation phenomenon observed when particles having different density and shape as well as particle diameter have been charged by mixing them were examined with the use of model experimental apparatus and a discrete simulation model. In consequence of it, it has come out that the segregation phenomenon of mixed particles depends on both of the particle diameter ratio and density ratio with a base particle and dependency can be estimated with a theoretical model.

    DOI: 10.2355/tetsutohagane1955.88.12_823

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  • Analysis of EEG data by multidimensional scaling

    Taguchi Y-h., Oono Yoshitsugu

    Meeting abstracts of the Physical Society of Japan   56 ( 2 )   193 - 193   2001.9

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  • Namerical Simulation of vibrated bed with two kinds particle

    Saitou Shinji, Taguchi Y-h

    Meeting abstracts of the Physical Society of Japan   56 ( 2 )   229 - 229   2001.9

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  • Analysis of questionnaire on lectures II

    Taguchi Y-h.

    Meeting abstracts of the Physical Society of Japan   56 ( 2 )   283 - 283   2001.9

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  • Numerical Simulation of Quantum Computation on quantum dots

    Yamanaka Masao, Taguchi Y-h

    Meeting abstracts of the Physical Society of Japan   56 ( 2 )   72 - 72   2001.9

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  • Classification study of AAA protein super family by MDS

    Taguchi Y-h., Oono Yoshitsugu

    Meeting abstracts of the Physical Society of Japan   56 ( 2 )   193 - 193   2001.9

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  • Diffusion and Reactions in Fractals and Disordered Systems

    田口善弘

    日本物理学会誌   56 ( 7 )   p.538   2001.7

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  • 粉粒体の混合と偏折における非平衡パターン

    T. シンブロー, 田口善弘訳

    パリティ   16 ( 7 )   4 - 12   2001.7

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  • オンデマンド販売と粉体技術

    田口善弘

    粉体と工業   33 ( 7 )   62 - 66   2001.7

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  • 非計量多次元尺度法への期待と新しい視点

    田口善弘, 大野克嗣, 横山和成

    統計数理   49 ( 1 )   133 - 153   2001.4

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  • Analysis of questionnaire on lectures

    Taguchi h.Y.

    Meeting abstracts of the Physical Society of Japan   56 ( 1 )   339 - 339   2001.3

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  • Modeling of food webs and stable structures in parameter space of species functions

    Kizaki Shinya, Taguchi Y-h.

    Meeting abstracts of the Physical Society of Japan   56 ( 1 )   272 - 272   2001.3

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  • G. H. Ristow, Pattern Formation in Granular Materials, Spring-Verlag, Berlin and Heidelberg, 2000, xiii+161p., 24×16cm, \13,350, (Springer Tracts in Modern Physics,Vol.164), [大学院向・専門書] : J. Duran, Sand, Powders and Grains; An Introduction to the Physics of Granular Materials, Spring-Verlag, New York and Berlin, 2000, xiii+214p., 24×15.5cm, \10,320, [大学院向・専門書]

    田口 善弘

    日本物理學會誌   56 ( 1 )   52 - 53   2001.1

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  • Pattern Formation in Granular Materials

    田口善弘

    日本物理学会誌   56 ( 1 )   52 - 53   2001.1

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  • BOOK REVIEW -SCIENCE-

    田口善弘

    JN   104 ( 12 )   p.115   2000.12

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  • 右と左

    田口善弘

    数学セミナー   39 ( 12 )   p.42   2000.12

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  • 相転移の社会学

    田口善弘

    中央評論   2000.12

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  • BOOK REVIEW -SCIENCE-

    田口善弘

    JN   104 ( 11 )   p.115   2000.11

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  • BOOK REVIEW -SCIENCE-

    田口善弘

    JN   104 ( 10 )   p.119   2000.10

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  • BOOK REVIEW -SCIENCE-

    田口善弘

    JN   104 ( 9 )   p.115   2000.9

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  • BOOK REVIEW -SCIENCE-

    田口善弘

    JN   104 ( 8 )   p.115   2000.8

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  • BOOK REVIEW -SCIENCE-

    田口善弘

    JN   104 ( 7 )   p.123   2000.7

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  • アンケートにみる中央大学物理学科の新入生像

    岩崎考志, 田口善弘

    大学の物理教育   2000 ( 2 )   43 - 46   2000.7

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  • BOOK REVIEW -SCIENCE-

    田口善弘

    JN   104 ( 6 )   p.115   2000.6

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  • BOOK REVIEW -SCIENCE-

    田口善弘

    JN   104 ( 5 )   p.115   2000.5

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  • BOOK REVIEW -SCIENCE-

    田口善弘

    JN   104 ( 4 )   p.131   2000.4

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  • BOOK REVIEW -SCIENCE-

    田口善弘

    JN   104 ( 3 )   p.82   2000.3

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  • Analysis of discharging and stuck phenomena of burden using discreet model (Development of burden distribution model using discreet model -2)

    MATSUZAKI Shinroku

    13 ( 1 )   p.138 - 138   2000.3

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  • BOOK REVIEW -SCIENCE-

    田口善弘

    JN   104 ( 2 )   p.82   2000.2

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  • 認識論、進化論などに量子力学を加えた万物の理論提唱

    田口善弘

    日経サイエンス   30 ( 2 )   p.133   2000.2

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  • BOOK REVIEW -SCIENCE-

    田口善弘

    JN   104 ( 1 )   p.82   2000.1

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  • ガリレオ、ニュートン、カオスやフラクタル、気象の問題までを列挙

    田口善弘

    日経サイエンス   29 ( 11 )   p.125   1999.11

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  • 24pU-8 Numerical Simulation of Inverse Segregation

    SAITOU Shinji, OHYAMA Yasushi, TAGUCHI Y-h

    Meeting abstracts of the Physical Society of Japan   54 ( 2 )   207 - 207   1999.9

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  • 27aU-7 Analysis of diversity of soil micro biology by multidimensional scaling

    TAGUCHI Y-h, YOKOYAMA Kazunari, OONO Yoshitsugu

    Meeting abstracts of the Physical Society of Japan   54 ( 2 )   279 - 279   1999.9

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  • 粉粒体の物理学

    西森拓, 早川尚男, 田口善弘

    新装版物理学論文選集シリーズ   1999.9

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  • インターネットとマルチメディア

    田口善弘

    粉体と工業   31 ( 9 )   32 - 37   1999.9

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  • 果して物理学は生物を理解できるか

    田口善弘

    日経サイエンス   29 ( 8 )   p.147   1999.8

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  • 7.大学の立場から

    田口善弘

    物理教育   47 ( 3 )   p.160 - 160   1999.6

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    DOI: 10.20653/pesj.47.3_160

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  • Analysis of segregation property of burden using discreet model (Development of burden distribution model using discreet model-1)

    MATSUZAKI Shinroku

    CAMP-ISIJ   12 ( 1 )   127 - 127   1999.3

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  • 砂時計の七不思議

    田口善弘

    技術と経済   383 ( 383 )   64 - 68   1999.1

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  • 波・熱伝導方程式から生まれたフーリェ展開

    田口善弘

    数学セミナー   8 - 11   1998.12

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  • 数値計算ツールとそのKnow-How 第4回HDFのススメ

    田口善弘

    LinuxJapan   118 - 121   1998.7

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  • はじめての講義 3つの法則

    田口善弘

    大学の物理教育   1998 ( 2 )   1998.7

  • 科学の終焉

    田口善弘

    科学   68 ( 3 )   p.268   1998.3

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  • 数値計算ツールとそのKnow-How 第2回 HPF on Linux

    田口善弘

    LinuxJapan   6   102 - 107   1998

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  • 数値計算ツールとそのKnow-How 第3回 Pro Fortran

    田口善弘

    LinuxJapan   7   110 - 113   1998

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  • 複雑系-自然の不思議・社会の不思議の解明

    田口善弘

    bit別冊 「インターネット時代の数学」   227 - 235   1997.11

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  • どうして、砂なんて研究するの?

    田口善弘

    数学セミナー   36 ( 9 )   18 - 22   1997.9

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  • 1年生の力学の講義

    田口善弘

    大学の物理教育   97 ( 2 )   p.52   1997.7

  • 道楽科学者列伝

    田口善弘

    技術と経済   ( 365 )   p.51   1997.7

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  • ネアンデルタールとは誰か

    田口善弘

    技術と経済   ( 364 )   p.67   1997.6

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  • 時間について -アインシュタインが残した謎とパラドックス-

    田口善弘

    技術と経済   ( 363 )   p.64   1997.5

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  • 科学を計る

    田口善弘

    技術と経済   ( 362 )   p.56   1997.4

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  • ニワトリの歯

    田口善弘

    技術と経済   ( 361 )   p.51   1997.3

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  • ロバートフック ニュートンに消された男

    田口善弘

    技術と経済   ( 360 )   p.52   1997.2

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  • 昆虫の誕生

    田口善弘

    技術と経済   ( 359 )   p.52   1997.1

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  • コミュニケーション過去・現在・未来

    田口善弘

    LinuxJapan   4   p.172   1997

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  • 数値演算ツールやそのノウハウ ~第一回:PMV on Linux~

    田口善弘

    LinuxJapan   5   -73   1997

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  • 数値計算機としてのLinuxマシン

    田口善弘

    LinuxJapan   2   p.136   1997

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  • Linuxで見る砂粒の科学 --- 粉粒体の動力学

    田口善弘

    LinuxJapan   3   p.5   1997

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  • サイバースペースの著作権

    田口善弘

    技術と経済   ( 358 )   p.55   1996.12

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  • 粉のふるまいの科学

    田口善弘

    化学工学   60 ( 11 )   844 - 845   1996.11

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  • 機械の中の幽霊

    田口善弘

    技術と経済   ( 357 )   p.67   1996.11

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  • 科学論入門

    田口善弘

    技術と経済   ( 356 )   p.73   1996.10

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  • プライバシーはパズルで守る

    田口善弘

    ワイアード   2 ( 9 )   109 - 113   1996.9

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  • 複雑系

    田口善弘

    技術と経済   ( 355 )   1996.9

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  • Velocity distribution of inelastic collision spheres under shear

    TAGUCHI Y-h., IDA Shigeru

    15   213 - 214   1996.7

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  • 物理の樹

    田口善弘

    中央評論   48 ( 2 )   p.131   1996.6

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  • カーマーカー特許とソフトウェア

    田口善弘

    技術と経済   352   p.69   1996.6

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  • 情報ハイウェイは大渋滞!!

    田口善弘

    ワイアード   2 ( 5 )   90 - 95   1996.5

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  • 粉体は数理科学の対象となりうるか

    早川尚男, 田口善弘

    数理科学   34 ( 4 )   78 - 85   1996.4

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  • 誰がどうやってコンピュータを創ったのか?

    田口善弘

    日本物理学会誌   51 ( 3 )   p.218 - 218   1996.3

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  • 紙切れに価値があるわけ

    田口善弘

    ワイアード   2 ( 1 )   98 - 104   1996.1

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  • 生命プログラムのハッカーたち

    田口善弘

    ワイアード   1 ( 11 )   104 - 108   1995.11

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  • Interstellar and granular turbulence

    Taguchi Yoshihiro, Ida Shigeru, Takayasu Hideki

    Abstracts of the meeting of the Physical Society of Japan. Sectional meeting   1995 ( 3 )   664 - 664   1995.9

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  • Stochastic shell model : 乱流におけるanomalousスケーリングと非ガウス分布の一つのモデル(流れの安定性と乱流統計)

    高安 秀樹, 田口 善弘, 勝山 智男

    数理解析研究所講究録   921   89 - 94   1995.8

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  • カオスと知的情報処理; カオスは本当に役に立つのか

    田口善弘

    日本物理学会誌   50 ( 6 )   p.482 - 482   1995.6

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  • Dynamics of Granular Matter from the Physical Point of View. (III).

    TAGUCHI Yoshihiro

    Journal of the Research Association of Powder Technology, Japan   32 ( 6 )   412 - 418   1995.6

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    Language:Japanese   Publisher:The Society of Powder Technology, Japan  

    DOI: 10.4164/sptj.32.412

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  • Dynamics of Granular Matter from the Physical Point of View.

    TAGUCHI Yoshihiro

    Journal of the Research Association of Powder Technology, Japan   32 ( 5 )   330 - 336   1995.5

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    DOI: 10.4164/sptj.32.330

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  • Dyamics of Granular Matter from the Physical Point of View(I).

    TAGUCHI Yoshihiro

    Journal of the Research Association of Powder Technology, Japan   32 ( 4 )   240 - 246   1995.4

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    DOI: 10.4164/sptj.32.240

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  • Proposal of ideal granular matter

    Taguchi Yoshihiro, Takayasu Hideki

    Abstracts of the meeting of the Physical Society of Japan. Annual meeting   50 ( 3 )   666 - 666   1995.3

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  • カオス : 複雑さに内在する規則性

    田口善弘

    日本物理学会誌   50 ( 1 )   p.55   1995.1

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  • 3a-C-4 Non-Gaussian distribution in granular turbulence

    Taguchi Yoshihiro, Takayasu Hideki

    Abstracts of the meeting of the Physical Society of Japan. Sectional meeting   1994 ( 3 )   452 - 452   1994.8

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  • 2p-YB-6 Numerical model of non-Gaussian distributions in the fluid turbulence

    Taguchi Yoshihiro, Takayasu Hideki, Takayasu Misako

    Abstracts of the meeting of the Physical Society of Japan. Sectional meeting   1994 ( 4 )   67 - 67   1994.8

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  • A. Mehta, ed., Granular Matter; An Interdisciplinary Apprach, Springer-Verlag, Berlin and Hidelberg, 1994, xi+306p., 24×16cm, 15,800円

    49 ( 8 )   672 - 672   1994.8

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  • “砂上の桜閣”の物理学

    田口善弘

    科学   64 ( 8 )   504 - 511   1994.8

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    Language:Japanese   Publisher:岩波書店  

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  • 粉粒体は「流れる」か?

    田口善弘

    固体物理   29 ( 5 )   451 - 458   1994.5

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  • 29p-B-2 1/f fluctuation of lattice vibrations with weak non-linear interaction by the FPU equation II

    Fukamachi Kenichi, Taguchi Yoshihiro, Kawai Toshio

    Abstracts of the meeting of the Physical Society of Japan. Annual meeting   49 ( 3 )   534 - 534   1994.3

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    Language:Japanese   Publisher:The Physical Society of Japan (JPS)  

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  • 28p-C-11 Turbulence in vibrated bed of powder

    Taguchi Yoshihiro

    Abstracts of the meeting of the Physical Society of Japan. Annual meeting   49 ( 3 )   517 - 517   1994.3

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    Language:Japanese   Publisher:The Physical Society of Japan (JPS)  

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  • 粉体の物理とパターン形成

    早川尚男, 西森拓, 佐々真一, 田口善弘

    日本物理学会誌   49 ( 1 )   18 - 25   1994.1

  • 格子渦管モデルとランダム移流項モデルと流体乱流 (統計数理研究所 研究活動 (研究会報告 乱流の統計理論とその応用))

    田口 善弘

    統計数理   41 ( 2 )   247 - 247   1993.12

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