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

Background/Objectives: Amyotrophic Lateral Sclerosis is a progressive neurodegenerative disorder characterized by the loss of upper and lower motor neurons. Key factors contributing to neuronal death include mitochondrial energy damage, oxidative stress, and excitotoxicity. The frontal cortex is crucial for action initiation, planning, and voluntary movements whereas the spinal cord facilitates communication with the brain, walking, and reflexes. By investigating transcriptome data from the frontal cortex and spinal cord, we aim to elucidate common pathological mechanisms and pathways involved in ALS for understanding the disease progression and identifying potential therapeutic targets. Methods: In this study, we quantified gene and transcript expression patterns, predicted variants, and assessed their functional effects using computational tools. It also includes predicting variant-associated regulatory effects, constructing functional interaction networks, and performing a gene enrichment analysis. Results: We found novel genes for the upregulation of immune response, and the downregulation of metabolic-related and defective degradation processes in both the spinal cord and frontal cortex. Additionally, we observed the dysregulation of histone regulation and blood pressure-related genes specifically in the frontal cortex. Conclusions: These results highlight the distinct and shared molecular disruptions in ALS, emphasizing the critical roles of immune response and metabolic dysfunction in neuronal degeneration. Targeting these pathways may provide new therapeutic avenues to combat neurodegeneration and preserve neuronal health.

DOI： 10.3390/genes15111431

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Language：English   Publishing type：Part of collection (book)   Publisher：WORLD SCIENTIFIC  

DOI： 10.1142/9789811293269_0014

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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)  

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)  

DOI： 10.1007/s10265-018-1017-x

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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.  

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  

DOI： 10.3390/ijms19051318

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

DOI： 10.1016/j.ygeno.2018.05.024

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

DOI： 10.1186/s12920-017-0302-1

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

DOI： 10.3390/genes8100243

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

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)  

DOI： 10.1038/s41598-017-10275-4

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

DOI： 10.1371/journal.pone.0183933

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

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)  

DOI： 10.1038/srep44016

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

DOI： 10.1080/14397595.2017.1285845

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

DOI： 10.1007/978-3-319-63312-1_45

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

DOI： 10.1016/j.nepig.2016.10.001

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

DOI： 10.1186/s12920-016-0196-3

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

DOI： 10.1016/j.gpb.2016.04.004

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

DOI： 10.1186/s13040-016-0101-9

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

DOI： 10.3390/ijms17050696

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

DOI： 10.1111/hepr.12518

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

DOI： 10.1109/BIBE.2016.14

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

DOI： 10.1186/1471-2105-16-S18-S16

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

DOI： 10.1038/srep16294

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

DOI： 10.1038/srep17209

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

DOI： 10.1186/s12859-015-0574-4

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

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)  

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  

DOI： 10.2197/ipsjtbio.8.14

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

DOI： 10.4137/EBO.S21832

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

DOI： 10.1186/1471-2164-15-S9-S2

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

DOI： 10.4018/978-1-4666-6611-5.ch007

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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.  

DOI： 10.1186/1756-0500-7-581

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

DOI： 10.1186/1752-0509-8-S1-S4

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

DOI： 10.2197/ipsjtbio.7.2

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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)  

DOI： 10.2174/09298665113209990052

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

DOI： 10.1371/journal.pone.0066714

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

DOI： 10.1007/978-3-642-39678-6_38

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

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DOI： 10.1371/journal.pone.0048366

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DOI： 10.1007/978-3-642-24553-4_19

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DOI： 10.1007/978-3-642-34123-6_13

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

DOI： 10.3390/ijms12129265

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DOI： 10.1109/bibmw.2011.6112516

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DOI： 10.1109/BIBMW.2011.6112542

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DOI： 10.1109/BIBMW.2011.6112520

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DOI： 10.1109/BIBMW.2011.6112515

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DOI： 10.1109/BIBMW.2011.6112521

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DOI： 10.1109/BIBMW.2011.6112517

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DOI： 10.1007/978-3-642-14922-1_84

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DOI： 10.2197/ipsjtbio.1.35

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

DOI： 10.1109/IJCNN.2008.4633857

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

DOI： 10.1186/1471-2105-8-404

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DOI： 10.1007/s10035-005-0215-0

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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)  

DOI： 10.1093/bioinformatics/bti067

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DOI： 10.7566/jpsjnc.2.02

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DOI： 10.1002/0471712531.ch18

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DOI： 10.1143/JPSJ.72.3002

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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)  

DOI： 10.2355/tetsutohagane1955.88.12_823

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

DOI： 10.1016/0378-4371(96)00025-8

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

DOI： 10.1142/S0218348X96000352

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

DOI： 10.1209/0295-5075/30/8/010

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DOI： 10.1143/JJAP.34.397

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

DOI： 10.1016/0167-2789(95)90059-4

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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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DOI： 10.1016/0921-5093(94)90989-X

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Language：English   Publishing type：Research paper (bulletin of university, research institution)  

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)  

DOI： 10.1163/156855294x00375

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DOI： 10.1016/0167-2789(93)90100-F

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DOI： 10.1142/S0218348X93001192

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DOI： 10.1209/0295-5075/24/3/008

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DOI： 10.1142/S0217979293003887

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DOI： 10.1142/s0217979293002626

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DOI： 10.1051/jp2:1992255

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DOI： 10.1103/PhysRevLett.69.1367

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DOI： 10.1103/PhysRevA.41.2249

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DOI： 10.1016/0378-4371(89)90018-6

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DOI： 10.1088/0305-4470/21/22/024

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DOI： 10.1088/0305-4470/21/8/031

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DOI： 10.1088/0305-4470/21/3/043

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DOI： 10.1088/0305-4470/20/18/058

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DOI： 10.1088/0022-3719/20/30/013

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DOI： 10.1143/PTP.77.775

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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)  

DOI： 10.1143/JPSJ.55.1458

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DOI： 10.1143/JPSJ.55.656

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DOI： 10.1143/JPSJ.55.323

DOI： 10.1143/jpsj.55.323

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DOI： 10.1143/PTPS.87.23

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DOI： 10.1143/PTPS.87.247

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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

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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：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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Total pages：viii, 291p   Language：Japanese  

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

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

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

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