Language：English   Publisher：CRC Press  

DOI： 10.1201/9781003424987

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

Transmission electron microscopy imaging provides a unique opportunity to inspect the detailed structure of infected lung cells with SARS-CoV-2. Unlike previous studies, this novel study aims to investigate COVID-19 classification at the lung cellular level in response to SARS-CoV-2. Particularly, differentiating between healthy and infected human alveolar type II (hAT2) cells with SARS-CoV-2. Hence, we explore the feasibility of deep transfer learning (DTL) and introduce a highly accurate approach that works as follows: First, we downloaded and processed 286 images pertaining to healthy and infected hAT2 cells obtained from the electron microscopy public image archive. Second, we provided processed images to two DTL computations to induce ten DTL models. The first DTL computation employs five pre-trained models (including DenseNet201 and ResNet152V2) trained on more than one million images from the ImageNet database to extract features from hAT2 images. Then, it flattens and provides the output feature vectors to a trained, densely connected classifier with the Adam optimizer. The second DTL computation works in a similar manner, with a minor difference in that we freeze the first layers for feature extraction in pre-trained models while unfreezing and jointly training the next layers. The results using five-fold cross-validation demonstrated that TFeDenseNet201 is 12.37× faster and superior, yielding the highest average ACC of 0.993 (F1 of 0.992 and MCC of 0.986) with statistical significance (P<2.2×10−16 from a t-test) compared to an average ACC of 0.937 (F1 of 0.938 and MCC of 0.877) for the counterpart (TFtDenseNet201), showing no significance results (P=0.093 from a t-test).

DOI： 10.3390/math12101573

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

Understanding breast cancer drug response mechanisms can play a crucial role in improving treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient support vector machine (esvm) working as follows: First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in its dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, which are provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, including deep learning, results demonstrate the superiority and efficiency of esvm, achieving high-performance results and having more expressed genes in well-established breast cancer cell lines, including MD-MB231, MCF7, and HS578T. Moreover, esvm is able to identify (1) various drugs, including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanisms, progression, and metastasizing. Our method is available publicly on the maGENEgerZ web server.

DOI： 10.3390/math12101536

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DOI： 10.1101/2024.01.25.577291

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

DOI： 10.1016/j.engappai.2023.106607

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

DOI： 10.1109/icbcb57893.2023.10246633

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

DOI： 10.1016/j.ygeno.2023.110577

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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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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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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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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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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

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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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

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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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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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

PubMed

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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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Other Link： https://www.eurekaselect.com/208449/article

Publisher：Elsevier  

DOI： 10.1016/b978-0-323-89774-7.00020-0

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Publisher：Springer Nature Singapore  

DOI： 10.1007/978-981-16-9158-4_7

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Publisher：Springer Nature Singapore  

DOI： 10.1007/978-981-16-9158-4_1

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Publisher：Springer Nature Singapore  

DOI： 10.1007/978-981-16-9158-4

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Publisher：Springer Nature Singapore  

DOI： 10.1007/978-981-16-9158-4_11

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

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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DOI： 10.1101/2022.04.29.490081

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

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

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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Other Link： https://link.springer.com/article/10.1186/s12920-022-01181-4/fulltext.html

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

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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Authorship：Last author   Publishing type：Part of collection (book)   Publisher：Springer International Publishing  

DOI： 10.1007/978-3-030-91608-4_34

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

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

DOI： 10.3390/genes12091442

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Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

<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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Other Link： https://www.nature.com/articles/s41598-021-95698-w

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

DOI： 10.1101/2021.06.15.448531

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

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

DOI： 10.1016/j.ejps.2021.105742

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

DOI： 10.1016/j.compbiomed.2021.104257

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

<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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Other Link： http://www.nature.com/articles/s41598-021-87779-7

Publishing type：Research paper (scientific journal)   Publisher：Frontiers Media SA  

DOI： 10.3389/fneur.2021.662373

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

DOI： 10.1016/j.knosys.2021.106834

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

DOI： 10.3389/fgene.2021.639160

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

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

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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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

<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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Other Link： http://www.nature.com/articles/s41598-020-78500-1

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

<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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Other Link： http://link.springer.com/article/10.1186/s41231-020-00078-7/fulltext.html

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

DOI： 10.1038/s41598-020-71997-6

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

DOI： 10.1371/journal.pone.0238907

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

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

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

DOI： 10.1016/j.ygeno.2019.12.011

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

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

DOI： 10.1016/j.compbiomed.2020.103656

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

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.

DOI： 10.1038/s41598-019-56842-9

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Other Link： http://www.nature.com/articles/s41598-019-56842-9

Publishing type：Research paper (scientific journal)  

DOI： 10.20944/PREPRINTS202004.0431.V2

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Publishing type：Research paper (scientific journal)  

DOI： 10.20944/PREPRINTS202004.0431.V1

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Bentham Science Publishers Ltd.  

DOI： 10.2174/1381612825666191210160906

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

DOI： 10.1038/s41598-019-54782-y

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Language：English   Publishing type：Research paper (other academic)  

DOI： 10.1142/9789811203589_0010

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

DOI： 10.3389/fgene.2019.00864

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Publishing type：Research paper (scientific journal)   Publisher：Chem-Bio Informatics Society  

DOI： 10.1273/cbij.19.32

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Language：English   Publishing type：Research paper (other academic)   Publisher：Elsevier  

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

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Authorship：Lead author   Language：English   Publishing type：Part of collection (book)   Publisher：Elsevier  

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

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

DOI： 10.1016/j.eswa.2019.02.013

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-030-26763-6_54

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DOI： 10.3390/books978-3-03897-769-8

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Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media {LLC}  

DOI： 10.1186/s12859-018-2395-8

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

DOI： 10.3390/cells7120245

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Authorship：Lead author,　Corresponding author  

DOI： 10.1109/BIBE.2018.00045

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Publisher：Springer International Publishing  

DOI： 10.1007/978-3-319-95933-7_90

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

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.

DOI： 10.1007/s10265-018-1017-x

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Publishing type：Research paper (scientific journal)  

DOI： 10.1371/journal.pone.0200451

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

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

DOI： 10.3390/cells7060054

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：BioMed Central Ltd.  

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.

DOI： 10.1186/s12859-018-2068-7

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

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.

DOI： 10.3390/ijms19051318

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Academic Press Inc.  

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.

DOI： 10.1016/j.ygeno.2018.05.024

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：BIOMED CENTRAL LTD  

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.

DOI： 10.1186/s12920-017-0302-1

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

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.

DOI： 10.3390/genes8100243

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：NATURE PUBLISHING GROUP  

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.

DOI： 10.1038/s41598-017-13003-0

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DOI： 10.1109/BIBE.2017.00-66

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

Language：English   Publishing type：Research paper (scientific journal)   Publisher：NATURE PUBLISHING GROUP  

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.

DOI： 10.1038/s41598-017-10275-4

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：PUBLIC LIBRARY SCIENCE  

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.

DOI： 10.1371/journal.pone.0183933

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

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.

DOI： 10.3390/ijms18081611

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Authorship：Lead author,　Corresponding author   Publisher：World Scientific  

DOI： 10.1142/9789813207981_0008

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

Language：English   Publishing type：Research paper (scientific journal)   Publisher：NATURE PUBLISHING GROUP  

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.

DOI： 10.1038/srep44016

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：TAYLOR & FRANCIS LTD  

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.

DOI： 10.1080/14397595.2017.1285845

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer Verlag  

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.

DOI： 10.1007/978-3-319-63312-1_45

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

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.

DOI： 10.1016/j.nepig.2016.10.001

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Language：English   Publisher：WILEY  

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：BIOMED CENTRAL LTD  

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.

DOI： 10.1186/s12920-016-0196-3

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

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.

DOI： 10.1016/j.gpb.2016.04.004

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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.

DOI： 10.1186/s13040-016-0101-9

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

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.

DOI： 10.3390/ijms17050696

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

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.

DOI： 10.1111/hepr.12518

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

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.

DOI： 10.1109/BIBE.2016.14

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：BIOMED CENTRAL LTD  

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.

DOI： 10.1186/1471-2105-16-S18-S16

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

Language：English   Publishing type：Research paper (scientific journal)   Publisher：NATURE PUBLISHING GROUP  

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.

DOI： 10.1038/srep16294

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：NATURE PUBLISHING GROUP  

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.

DOI： 10.1038/srep17209

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：WILEY-BLACKWELL  

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：BIOMED CENTRAL LTD  

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.

DOI： 10.1186/s12859-015-0574-4

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS  

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.

DOI： 10.1117/1.NPh.2.1.015002

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

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.

DOI： 10.1109/CIBCB.2015.7300274

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Information Processing Society of Japan  

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.

DOI： 10.2197/ipsjtbio.8.14

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SAGE PUBLICATIONS LTD  

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.

DOI： 10.4137/EBO.S21832

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

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：BIOMED CENTRAL LTD  

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.

DOI： 10.1186/1471-2164-15-S9-S2

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

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Part of collection (book)   Publisher：IGI Global  

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.

DOI： 10.4018/978-1-4666-6611-5.ch007

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：PUBLIC LIBRARY SCIENCE  

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.

DOI： 10.1371/journal.pone.0106314

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：BioMed Central Ltd.  

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.

DOI： 10.1186/1756-0500-7-581

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

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：BIOMED CENTRAL LTD  

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.

DOI： 10.1186/1752-0509-8-S1-S4

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

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

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.

DOI： 10.2197/ipsjtbio.7.2

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

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.

DOI： 10.1007/978-3-319-09330-7_52

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：BENTHAM SCIENCE PUBL LTD  

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.

DOI： 10.2174/09298665113209990052

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：PUBLIC LIBRARY SCIENCE  

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.

DOI： 10.1371/journal.pone.0066714

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：BIOMED CENTRAL LTD  

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.

DOI： 10.1186/1756-0381-6-11

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DOI： 10.14511/jasa.2013.020106

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：F1000 Research Ltd  

DOI： 10.12688/f1000research.2-21.v3

DOI： 10.12688/f1000research.2-21.v2

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Language：Japanese   Publisher：一般社団法人 日本アレルギー学会  

DOI： 10.15036/arerugi.62.1320_2

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SCITEPRESS  

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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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer Verlag  

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.

DOI： 10.1007/978-3-642-39678-6_38

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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.

DOI： 10.1371/journal.pone.0048366

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：INT SOC AGING & DISEASE  

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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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

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.

DOI： 10.1007/978-3-642-24553-4_19

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

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.

DOI： 10.1007/978-3-642-34123-6_13

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

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.

DOI： 10.3390/ijms12129265

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DOI： 10.1109/bibmw.2011.6112516

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

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.

DOI： 10.1109/BIBMW.2011.6112542

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

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.

DOI： 10.1109/BIBMW.2011.6112520

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

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.

DOI： 10.1109/BIBMW.2011.6112515

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

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.

DOI： 10.1109/BIBMW.2011.6112521

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

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.

DOI： 10.1109/BIBMW.2011.6112517

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

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

DOI： 10.1007/978-3-642-14922-1_84

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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.

DOI： 10.2197/ipsjtbio.1.35

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

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.

DOI： 10.1109/IJCNN.2008.4633857

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：BIOMED CENTRAL LTD  

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.

DOI： 10.1186/1471-2105-8-404

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPRINGER-VERLAG BERLIN  

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

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.

DOI： 10.1007/s10035-005-0215-0

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

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.

DOI： 10.1016/j.physd.2005.02.001

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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：OXFORD UNIV PRESS  

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.

DOI： 10.1093/bioinformatics/bti067

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DOI： 10.7566/jpsjnc.2.02

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Language：English   Publisher：JOHN WILEY & SONS INC  

DOI： 10.1002/0471712531.ch18

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：PHYSICAL SOC JAPAN  

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.

DOI： 10.1143/JPSJ.72.3002

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：PHYSICAL SOC JAPAN  

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.

DOI： 10.1143/JPSJ.72.2685

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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

DOI： 10.1016/S1474-6670(17)32452-7

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：IRON STEEL INST JAPAN KEIDANREN KAIKAN  

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

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.

DOI： 10.1016/0378-4371(96)00025-8

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：WORLD SCIENTIFIC PUBL CO PTE LTD  

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.

DOI： 10.1142/S0218348X96000352

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

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.

DOI： 10.1209/0295-5075/30/8/010

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Language：English   Publisher：JAPAN J APPLIED PHYSICS  

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.

DOI： 10.1143/JJAP.34.397

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

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.

DOI： 10.1016/0167-2789(95)90059-4

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Language：English   Publisher：WORLD SCIENTIFIC PUBL CO PTE LTD  

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.

DOI： 10.1142/S0217979294001676

DOI： 10.1142/s0217979294001676

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DOI： 10.1142/9789814503792_0064

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DOI： 10.1142/S0217984994001291

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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.

DOI： 10.1016/0921-5093(94)90989-X

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CiNii Books

CiNii Research

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Other Link： https://ndlsearch.ndl.go.jp/books/R000000004-I3860769

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

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.

DOI： 10.1163/156855294x00375

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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.

DOI： 10.1016/0167-2789(93)90100-F

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：WORLD SCIENTIFIC PUBL CO PTE LTD  

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.

DOI： 10.1142/S0218348X93001192

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

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.

DOI： 10.1209/0295-5075/24/3/008

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Language：English   Publisher：WORLD SCIENTIFIC PUBL CO PTE LTD  

DOI： 10.1142/S0217979293003887

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：WORLD SCIENTIFIC PUBL CO PTE LTD  

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.

DOI： 10.1142/s0217979293002626

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMERICAN PHYSICAL SOC  

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

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.

DOI： 10.1051/jp2:1992255

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMERICAN PHYSICAL SOC  

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.

DOI： 10.1103/PhysRevLett.69.1367

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

Language：English   Publisher：AMER PHYSICAL SOC  

DOI： 10.1103/PhysRevA.41.2249

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

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

DOI： 10.1016/0378-4371(89)90018-6

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

Language：English   Publishing type：Research paper (scientific journal)   Publisher：IOP PUBLISHING LTD  

DOI： 10.1088/0305-4470/21/22/024

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Language：English   Publisher：IOP PUBLISHING LTD  

DOI： 10.1088/0305-4470/21/8/031

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Language：English   Publisher：IOP PUBLISHING LTD  

DOI： 10.1088/0305-4470/21/3/043

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

Language：English   Publisher：IOP PUBLISHING LTD  

DOI： 10.1088/0305-4470/20/18/058

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

Language：English   Publishing type：Research paper (scientific journal)   Publisher：IOP PUBLISHING LTD  

DOI： 10.1088/0022-3719/20/30/013

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

DOI： 10.1143/PTP.77.775

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

Language：English   Publisher：Progress of Theoretical Physics  

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.

DOI： 10.1143/PTPS.87.23

CiNii Books

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Other Link： http://dl.ndl.go.jp/info:ndljp/pid/10883276

Language：English   Publishing type：Research paper (scientific journal)   Publisher：PHYSICAL SOCIETY JAPAN  

DOI： 10.1143/JPSJ.55.1458

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

Language：English   Publishing type：Research paper (scientific journal)   Publisher：PHYSICAL SOCIETY JAPAN  

DOI： 10.1143/JPSJ.55.656

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

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

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：PHYSICAL SOCIETY JAPAN  

DOI： 10.1143/JPSJ.55.323

DOI： 10.1143/jpsj.55.323

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：PROGRESS THEORETICAL PHYSICS PUBLICATION OFFICE  

DOI： 10.1143/PTPS.87.23

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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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Language：English   Publisher：PHYSICAL SOCIETY JAPAN  

DOI： 10.1143/JPSJ.54.4494

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

DOI： 10.20944/preprints202004.0431.v2

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DOI： 10.20944/preprints202004.0431.v1

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DOI： 10.20944/preprints202004.0431.v2

DOI： 10.20944/PREPRINTS202004.0431.V1

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

ASIN

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Total pages：654   Book type：Scholarly book

ASIN

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Total pages：228   Responsible for pages：1-17,101-120,155-176   Language：English   Book type：Scholarly book

ASIN

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Total pages：228   Language：English   Book type：Scholarly book

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Total pages：530 p   Language：Japanese   Book type：Scholarly book

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Total pages：224   Language：Japanese  

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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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Total pages：224   Language：Japanese  

ASIN

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

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Total pages：339   Language：English   Book type：Scholarly book

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

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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

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

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Language：English   Book type：Scholarly book

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Total pages：viii, 291p   Language：Japanese  

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

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Total pages：v, 159p   Language：Japanese  

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Total pages：iv, 114p   Language：Japanese  

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

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

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

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Language：Japanese  

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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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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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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)   Publisher：Adjacent Digital Politics Ltd  

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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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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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)   Publisher：Adjacent Digital Politics Ltd  

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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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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Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)   Publisher：Adjacent Digital Politics Ltd  

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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Authorship：Lead author,　Corresponding author   Language：Japanese   Publishing type：Internal/External technical report, pre-print, etc.  

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Publisher：Adjacent Digital Politics Ltd  

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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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)  

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)  

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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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Authorship：Lead author,　Corresponding author   Language：English   Publisher：Bioconductor 3.17  

DOI： 10.18129/B9.bioc.TDbasedUFEadv

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DOI： 10.7875/togotv.2023.030

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

「《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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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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Language：English   Publisher：Bioconductor 3.17  

DOI： 10.18129/B9.bioc.TDbasedUFE

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Publisher：Adjacent Digital Politics Ltd  

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

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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  

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

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

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Authorship：Lead author,　Corresponding author   Publisher：Adjacent Digital Politics Ltd  

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

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Authorship：Lead author,　Corresponding author   Language：Japanese   Publishing type：Internal/External technical report, pre-print, etc.   Publisher：人工知能学会  

DOI： 10.11517/jsaifpai.121.0_07

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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  

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

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