2023/01/17 更新

イワタ ミチオ
岩田 通夫
IWATA Michio
Scopus 論文情報  
総論文数: 0  総Citation: 0  h-index: 7

Citation Countは当該年に発表した論文の被引用数

所属
大学院情報工学研究院 生命化学情報工学研究系
職名
准教授
外部リンク

取得学位

  • 九州大学  -  博士(農学)   2014年03月

学内職務経歴

  • 2022年09月 - 現在   九州工業大学   大学院情報工学研究院   生命化学情報工学研究系     准教授

論文

  • Regulome-based characterization of drug activity across the human diseasome 査読有り 国際誌

    Iwata M., Kosai K., Ono Y., Oki S., Mimori K., Yamanishi Y.

    npj Systems Biology and Applications   8 ( 1 )   2022年12月

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    担当区分:筆頭著者   記述言語:英語   掲載種別:研究論文(学術雑誌)

    Drugs are expected to recover the cell system away from the impaired state to normalcy through disease treatment. However, the understanding of gene regulatory machinery underlying drug activity or disease pathogenesis is far from complete. Here, we perform large-scale regulome analysis for various diseases in terms of gene regulatory machinery. Transcriptome signatures were converted into regulome signatures of transcription factors by integrating publicly available ChIP-seq data. Regulome-based correlations between diseases and their approved drugs were much clearer than the transcriptome-based correlations. For example, an inverse correlation was observed for cancers, whereas a positive correlation was observed for immune system diseases. After demonstrating the usefulness of the regulome-based drug discovery method in terms of accuracy and applicability, we predicted new drugs for nonsmall cell lung cancer and validated the anticancer activity in vitro. The proposed method is useful for understanding disease–disease relationships and drug discovery.

    DOI: 10.1038/s41540-022-00255-4

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  • Pathway trajectory analysis with tensor imputation reveals drug-induced single-cell transcriptomic landscape 査読有り 国際誌

    Iwata M., Mutsumine H., Nakayama Y., Suita N., Yamanishi Y.

    Nature Computational Science   2 ( 11 )   758 - 770   2022年11月

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    担当区分:筆頭著者   記述言語:英語   掲載種別:研究論文(学術雑誌)

    Genome-wide identification of single-cell transcriptomic responses of drugs in various human cells is a challenging issue in medical and pharmaceutical research. Here we present a computational method, tensor-based imputation of gene-expression data at the single-cell level (TIGERS), which reveals the drug-induced single-cell transcriptomic landscape. With this algorithm, we predict missing drug-induced single-cell gene-expression data with tensor imputation, and identify trajectories of regulated pathways considering intercellular heterogeneity. Tensor imputation outperformed existing imputation methods for data completion, and provided cell-type-specific transcriptomic responses for unobserved drugs. For example, TIGERS correctly predicted the cell-type-specific expression of maker genes for pancreatic islets. Pathway trajectory analysis of the imputed gene-expression profiles of all combinations of drugs and human cells identified single-cell-specific drug activities and pathway trajectories that reflect drug-induced changes in pathway regulation. The proposed method is expected to expand our understanding of the single-cell mechanisms of drugs at the pathway level.

    DOI: 10.1038/s43588-022-00352-8

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  • Small compound-based direct cell conversion with combinatorial optimization of pathway regulations 査読有り 国際誌

    Nakamura T., Iwata M., Hamano M., Eguchi R., Takeshita J.I., Yamanishi Y.

    Bioinformatics (Oxford, England)   38 ( 2 )   ii99 - ii105   2022年09月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    MOTIVATION: Direct cell conversion, direct reprogramming (DR), is an innovative technology that directly converts source cells to target cells without bypassing induced pluripotent stem cells. The use of small compounds (e.g. drugs) for DR can help avoid carcinogenic risk induced by gene transfection; however, experimentally identifying small compounds remains challenging because of combinatorial explosion. RESULTS: In this article, we present a new computational method, COMPRENDRE (combinatorial optimization of pathway regulations for direct reprograming), to elucidate the mechanism of small compound-based DR and predict new combinations of small compounds for DR. We estimated the potential target proteins of DR-inducing small compounds and identified a set of target pathways involving DR. We identified multiple DR-related pathways that have not previously been reported to induce neurons or cardiomyocytes from fibroblasts. To overcome the problem of combinatorial explosion, we developed a variant of a simulated annealing algorithm to identify the best set of compounds that can regulate DR-related pathways. Consequently, the proposed method enabled to predict new DR-inducing candidate combinations with fewer compounds and to successfully reproduce experimentally verified compounds inducing the direct conversion from fibroblasts to neurons or cardiomyocytes. The proposed method is expected to be useful for practical applications in regenerative medicine. AVAILABILITY AND IMPLEMENTATION: The code supporting the current study is available at the http://labo.bio.kyutech.ac.jp/~yamani/comprendre. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

    DOI: 10.1093/bioinformatics/btac475

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  • From drug repositioning to target repositioning: Prediction of therapeutic targets using genetically perturbed transcriptomic signatures 査読有り 国際誌

    Namba S., Iwata M., Yamanishi Y.

    Bioinformatics   38   I68 - I76   2022年07月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    Motivation: A critical element of drug development is the identification of therapeutic targets for diseases. However, the depletion of therapeutic targets is a serious problem. Results: In this study, we propose the novel concept of target repositioning, an extension of the concept of drug repositioning, to predict new therapeutic targets for various diseases. Predictions were performed by a trans-disease analysis which integrated genetically perturbed transcriptomic signatures (knockdown of 4345 genes and overexpression of 3114 genes) and disease-specific gene transcriptomic signatures of 79 diseases. The trans-disease method, which takes into account similarities among diseases, enabled us to distinguish the inhibitory from activatory targets and to predict the therapeutic targetability of not only proteins with known target-disease associations but also orphan proteins without known associations. Our proposed method is expected to be useful for understanding the commonality of mechanisms among diseases and for therapeutic target identification in drug discovery.

    DOI: 10.1093/bioinformatics/btac240

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  • TRANSDIRE: Data-driven direct reprogramming by a pioneer factor-guided trans-omics approach 査読有り 国際誌

    Eguchi R., Hamano M., Iwata M., Nakamura T., Oki S., Yamanishi Y.

    Bioinformatics   38 ( 10 )   2839 - 2846   2022年05月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    Motivation: Direct reprogramming involves the direct conversion of fully differentiated mature cell types into various other cell types while bypassing an intermediate pluripotent state (e.g. induced pluripotent stem cells). Cell differentiation by direct reprogramming is determined by two types of transcription factors (TFs): Pioneer factors (PFs) and cooperative TFs. PFs have the distinct ability to open chromatin aggregations, assemble a collective of cooperative TFs and activate gene expression. The experimental determination of two types of TFs is extremely difficult and costly. Results: In this study, we developed a novel computational method, TRANSDIRE (TRANS-omics-based approach for DIrect REprogramming), to predict the TFs that induce direct reprogramming in various human cell types using multiple omics data. In the algorithm, potential PFs were predicted based on low signal chromatin regions, and the cooperative TFs were predicted through a trans-omics analysis of genomic data (e.g. enhancers), transcriptome data (e.g. gene expression profiles in human cells), epigenome data (e.g. chromatin immunoprecipitation sequencing data) and interactome data. We applied the proposed methods to the reconstruction of TFs that induce direct reprogramming from fibroblasts to six other cell types: Hepatocytes, cartilaginous cells, neurons, cardiomyocytes, pancreatic cells and Paneth cells. We demonstrated that the methods successfully predicted TFs for most cell conversions with high accuracy. Thus, the proposed methods are expected to be useful for various practical applications in regenerative medicine.

    DOI: 10.1093/bioinformatics/btac209

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  • Epigenetic landscape of drug responses revealed through large-scale ChIP-seq data analyses 査読有り 国際誌

    Zou Z., Iwata M., Yamanishi Y., Oki S.

    BMC Bioinformatics   23 ( 1 )   2022年01月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    Background: Elucidating the modes of action (MoAs) of drugs and drug candidate compounds is critical for guiding translation from drug discovery to clinical application. Despite the development of several data-driven approaches for predicting chemical–disease associations, the molecular cues that organize the epigenetic landscape of drug responses remain poorly understood. Results: With the use of a computational method, we attempted to elucidate the epigenetic landscape of drug responses, in terms of transcription factors (TFs), through large-scale ChIP-seq data analyses. In the algorithm, we systematically identified TFs that regulate the expression of chemically induced genes by integrating transcriptome data from chemical induction experiments and almost all publicly available ChIP-seq data (consisting of 13,558 experiments). By relating the resultant chemical–TF associations to a repository of associated proteins for a wide range of diseases, we made a comprehensive prediction of chemical–TF–disease associations, which could then be used to account for drug MoAs. Using this approach, we predicted that: (1) cisplatin promotes the anti-tumor activity of TP53 family members but suppresses the cancer-inducing function of MYCs; (2) inhibition of RELA and E2F1 is pivotal for leflunomide to exhibit antiproliferative activity; and (3) CHD8 mediates valproic acid-induced autism. Conclusions: Our proposed approach has the potential to elucidate the MoAs for both approved drugs and candidate compounds from an epigenetic perspective, thereby revealing new therapeutic targets, and to guide the discovery of unexpected therapeutic effects, side effects, and novel targets and actions.

    DOI: 10.1186/s12859-022-04571-8

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  • The novel driver gene ASAP2 is a potential druggable target in pancreatic cancer 査読有り 国際誌

    Fujii A., Masuda T., Iwata M., Tobo T., Wakiyama H., Koike K., Kosai K., Nakano T., Kuramitsu S., Kitagawa A., Sato K., Kouyama Y., Shimizu D., Matsumoto Y., Utsunomiya T., Ohtsuka T., Yamanishi Y., Nakamura M., Mimori K.

    Cancer Science   112 ( 4 )   1655 - 1668   2021年04月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    Targeting mutated oncogenes is an effective approach for treating cancer. The 4 main driver genes of pancreatic ductal adenocarcinoma (PDAC) are KRAS, TP53, CDKN2A, and SMAD4, collectively called the “big 4” of PDAC, however they remain challenging therapeutic targets. In this study, ArfGAP with SH3 domain, ankyrin repeat and PH domain 2 (ASAP2), one of the ArfGAP family, was identified as a novel driver gene in PDAC. Clinical analysis with PDAC datasets showed that ASAP2 was overexpressed in PDAC cells based on increased DNA copy numbers, and high ASAP2 expression contributed to a poor prognosis in PDAC. The biological roles of ASAP2 were investigated using ASAP2-knockout PDAC cells generated with CRISPR-Cas9 technology or transfected PDAC cells. In vitro and in vivo analyses showed that ASAP2 promoted tumor growth by facilitating cell cycle progression through phosphorylation of epidermal growth factor receptor (EGFR). A repositioned drug targeting the ASAP2 pathway was identified using a bioinformatics approach. The gene perturbation correlation method showed that niclosamide, an antiparasitic drug, suppressed PDAC growth by inhibition of ASAP2 expression. These data show that ASAP2 is a novel druggable driver gene that activates the EGFR signaling pathway. Furthermore, niclosamide was identified as a repositioned therapeutic agent for PDAC possibly targeting ASAP2.

    DOI: 10.1111/cas.14858

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  • Omics-based Identification of Glycan Structures as Biomarkers for a Variety of Diseases 査読有り 国際誌

    Akiyoshi S., Iwata M., Berenger F., Yamanishi Y.

    Molecular Informatics   39 ( 1-2 )   2020年01月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    Glycans play important roles in cell communication, protein interaction, and immunity, and structural changes in glycans are associated with the regulation of a range of biological pathways involved in disease. However, our understanding of the detailed relationships between specific diseases and glycans is very limited. In this study, we proposed an omics-based method to investigate the correlations between glycans and a wide range of human diseases. We analyzed the gene expression patterns of glycogenes (glycosyltransferases and glycosidases) for 79 different diseases. A biological pathway-based glycogene signature was constructed to identify the alteration in glycan biosynthesis and the associated glycan structures for each disease state. The degradation of N-glycan and keratan sulfate, for example, may promote the growth or metastasis of multiple types of cancer, including endometrial, gastric, and nasopharyngeal. Our results also revealed that commonalities between diseases can be interpreted using glycogene expression patterns, as well as the associated glycan structure patterns at the level of the affected pathway. The proposed method is expected to be useful for understanding the relationships between glycans, glycogenes, and disease and identifying disease-specific glycan biomarkers.

    DOI: 10.1002/minf.201900112

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  • Network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets 査読有り 国際誌

    Iida M., Iwata M., Yamanishi Y.

    Bioinformatics   36   I516 - I524   2020年01月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    Motivation: Disease states are distinguished from each other in terms of differing clinical phenotypes, but characteristic molecular features are often common to various diseases. Similarities between diseases can be explained by characteristic gene expression patterns. However, most disease-disease relationships remain uncharacterized. Results: In this study, we proposed a novel approach for network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets. We performed large-scale analyses of omics data and molecular interaction networks for 79 diseases, including adrenoleukodystrophy, leukaemia, Alzheimer's disease, asthma, atopic dermatitis, breast cancer, cystic fibrosis and inflammatory bowel disease. We quantified disease-disease similarities based on proximities of abnormally expressed genes in various molecular networks, and showed that similarities between diseases could be explained by characteristic molecular network topologies. Furthermore, we developed a kernel matrix regression algorithm to predict the commonalities of drugs and therapeutic targets among diseases. Our comprehensive prediction strategy indicated many new associations among phenotypically diverse diseases.

    DOI: 10.1093/BIOINFORMATICS/BTAA439

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  • Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm 査読有り 国際誌

    Iwata M., Yuan L., Zhao Q., Tabei Y., Berenger F., Sawada R., Akiyoshi S., Hamano M., Yamanishi Y.

    Bioinformatics   35 ( 14 )   i191 - i199   2019年07月

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    担当区分:筆頭著者   記述言語:英語   掲載種別:研究論文(学術雑誌)

    Motivation: Genome-wide identification of the transcriptomic responses of human cell lines to drug treatments is a challenging issue in medical and pharmaceutical research. However, drug-induced gene expression profiles are largely unknown and unobserved for all combinations of drugs and human cell lines, which is a serious obstacle in practical applications. Results: Here, we developed a novel computational method to predict unknown parts of drug-induced gene expression profiles for various human cell lines and predict new drug therapeutic indications for a wide range of diseases. We proposed a tensor-train weighted optimization (TT-WOPT) algorithm to predict the potential values for unknown parts in tensor-structured gene expression data. Our results revealed that the proposed TT-WOPT algorithm can accurately reconstruct drug-induced gene expression data for a range of human cell lines in the Library of Integrated Network-based Cellular Signatures. The results also revealed that in comparison with the use of original gene expression profiles, the use of imputed gene expression profiles improved the accuracy of drug repositioning. We also performed a comprehensive prediction of drug indications for diseases with gene expression profiles, which suggested many potential drug indications that were not predicted by previous approaches. Supplementary information: Supplementary data are available at Bioinformatics online.

    DOI: 10.1093/bioinformatics/btz313

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  • KampoDB, database of predicted targets and functional annotations of natural medicines 査読有り 国際誌

    Sawada R., Iwata M., Umezaki M., Usui Y., Kobayashi T., Kubono T., Hayashi S., Kadowaki M., Yamanishi Y.

    Scientific Reports   8 ( 1 )   2018年12月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    Natural medicines (i.e., herbal medicines, traditional formulas) are useful for treatment of multifactorial and chronic diseases. Here, we present KampoDB (http://wakanmoview.inm.u-toyama.ac.jp/kampo/), a novel platform for the analysis of natural medicines, which provides various useful scientific resources on Japanese traditional formulas Kampo medicines, constituent herbal drugs, constituent compounds, and target proteins of these constituent compounds. Potential target proteins of these constituent compounds were predicted by docking simulations and machine learning methods based on large-scale omics data (e.g., genome, proteome, metabolome, interactome). The current version of KampoDB contains 42 Kampo medicines, 54 crude drugs, 1230 constituent compounds, 460 known target proteins, and 1369 potential target proteins, and has functional annotations for biological pathways and molecular functions. KampoDB is useful for mode-of-action analysis of natural medicines and prediction of new indications for a wide range of diseases.

    DOI: 10.1038/s41598-018-29516-1

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  • Predicting inhibitory and activatory drug targets by chemically and genetically perturbed transcriptome signatures 査読有り 国際誌

    Sawada R., Iwata M., Tabei Y., Yamato H., Yamanishi Y.

    Scientific Reports   8 ( 1 )   2018年12月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)

    Genome-wide identification of all target proteins of drug candidate compounds is a challenging issue in drug discovery. Moreover, emerging phenotypic effects, including therapeutic and adverse effects, are heavily dependent on the inhibition or activation of target proteins. Here we propose a novel computational method for predicting inhibitory and activatory targets of drug candidate compounds. Specifically, we integrated chemically-induced and genetically-perturbed gene expression profiles in human cell lines, which avoided dependence on chemical structures of compounds or proteins. Predictive models for individual target proteins were simultaneously constructed by the joint learning algorithm based on transcriptomic changes in global patterns of gene expression profiles following chemical treatments, and following knock-down and over-expression of proteins. This method discriminates between inhibitory and activatory targets and enables accurate identification of therapeutic effects. Herein, we comprehensively predicted drug-target-disease association networks for 1,124 drugs, 829 target proteins, and 365 human diseases, and validated some of these predictions in vitro. The proposed method is expected to facilitate identification of new drug indications and potential adverse effects.

    DOI: 10.1038/s41598-017-18315-9

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  • Pathway-Based Drug Repositioning for Cancers: Computational Prediction and Experimental Validation 査読有り 国際誌

    Iwata M., Hirose L., Kohara H., Liao J., Sawada R., Akiyoshi S., Tani K., Yamanishi Y.

    Journal of Medicinal Chemistry   61 ( 21 )   9583 - 9595   2018年11月

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    担当区分:筆頭著者   記述言語:英語   掲載種別:研究論文(学術雑誌)

    Developing drugs with anticancer activity and low toxic side-effects at low costs is a challenging issue for cancer chemotherapy. In this work, we propose to use molecular pathways as the therapeutic targets and develop a novel computational approach for drug repositioning for cancer treatment. We analyzed chemically induced gene expression data of 1112 drugs on 66 human cell lines and searched for drugs that inactivate pathways involved in the growth of cancer cells (cell cycle) and activate pathways that contribute to the death of cancer cells (e.g., apoptosis and p53 signaling). Finally, we performed a large-scale prediction of potential anticancer effects for all the drugs and experimentally validated the prediction results via three in vitro cellular assays that evaluate cell viability, cytotoxicity, and apoptosis induction. Using this strategy, we successfully identified several potential anticancer drugs. The proposed pathway-based method has great potential to improve drug repositioning research for cancer treatment.

    DOI: 10.1021/acs.jmedchem.8b01044

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  • Evaluation of an S-system root-finding method for estimating parameters in a metabolic reaction model 査読有り 国際誌

    Iwata M., Miyawaki-Kuwakado A., Yoshida E., Komori S., Shiraishi F.

    Mathematical Biosciences   301   21 - 31   2018年07月

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    担当区分:筆頭著者   記述言語:英語   掲載種別:研究論文(学術雑誌)

    In a mathematical model, estimation of parameters from time-series data of metabolic concentrations in cells is a challenging task. However, it seems that a promising approach for such estimation has not yet been established. Biochemical Systems Theory (BST) is a powerful methodology to construct a power-law type model for a given metabolic reaction system and to then characterize it efficiently. In this paper, we discuss the use of an S-system root-finding method (S-system method) to estimate parameters from time-series data of metabolite concentrations. We demonstrate that the S-system method is superior to the Newton–Raphson method in terms of the convergence region and iteration number. We also investigate the usefulness of a translocation technique and a complex-step differentiation method toward the practical application of the S-system method. The results indicate that the S-system method is useful to construct mathematical models for a variety of metabolic reaction networks.

    DOI: 10.1016/j.mbs.2018.01.010

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  • Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics 査読有り 国際誌

    Iwata M., Sawada R., Iwata H., Kotera M., Yamanishi Y.

    Scientific Reports   7   2017年01月

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    担当区分:筆頭著者   記述言語:英語   掲載種別:研究論文(学術雑誌)

    The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines. First, we performed pathway enrichment analyses of regulated genes to reveal active pathways among 163 biological pathways. Next, we explored potential target proteins (including primary targets and off-targets) with cell-specific transcriptional similarity using chemical-protein interactome. Finally, we predicted new therapeutic indications for 461 diseases based on the target proteins. We showed the usefulness of the proposed approach in terms of prediction coverage, interpretation, and large-scale applicability, and validated the new prediction results experimentally by an in vitro cellular assay. The approach has a high potential for advancing drug discovery and repositioning.

    DOI: 10.1038/srep40164

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▼全件表示

著書

  • The use of large-scale chemically-induced transcriptome data acquired from lincs to study small molecules

    Iwata M., Yamanishi Y.(共著)

    Methods in Molecular Biology  2019年01月 

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    記述言語:英語

    Identification of the modes of action of bioactive compounds is an important issue in chemical systems biology. In this chapter we review a recently developed data-driven approach using large-scale chemically induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures to elucidate the modes of action of bioactive compounds. First, we present a method for pathway enrichment analyses of regulated genes to reveal biological pathways activated by compounds. Next, we present a method using the pre-knowledge on chemical–protein interactome for predicting potential target proteins, including primary targets and off-targets, with transcriptional similarity. Finally, we present a method based on the target proteins for predicting new therapeutic indications for a variety of diseases. These approaches are expected to be useful for mode-of-action analysis, drug discovery, and drug repositioning.

    DOI: 10.1007/978-1-4939-8891-4_11

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