Publications

Original articles

2023

  1. Akazawa, N.; Nakamura, M.; Eda, N.; Murakami, H.; Nakagata, T.; Nanri, H.; Park, J.; Hosomi, K.; Mizuguchi, K.; Kunisawa, J.; Miyachi, M.; Hoshikawa, M.
    Gut microbiota alternation with training periodization and physical fitness in Japanese elite athletes
    Frontiers in Sports and Active Living 2023, 5
    https://doi.org/10.3389/fspor.2023.1219345
  2. Kawashima, H.; Watanabe, R.; Esaki, T.; Kuroda, M.; Nagao, C.; Kitatani, Y.N.; Ohashi, R.; Komura, H.; Mizuguchi, K.
    DruMAP: A Novel Drug Metabolism and Pharmacokinetics Analysis Platform
    Journal of Medicinal Chemistry 2023
    https://doi.org/10.1021/acs.jmedchem.3c00481
  3. Alarabi, A.B.; Mohsen, A.; Taleb, Z.B.; Mizuguchi, K.; Alshbool, F.Z.; Khasawneh, F.T.
    Predicting thrombotic cardiovascular outcomes induced by waterpipe-associated chemicals using comparative toxicogenomic database: Genes, phenotypes, and pathways
    Life Sciences 2023, 323121694-121694
    https://doi.org/10.1016/j.lfs.2023.121694
  4. Martin, ; Watanabe, R.; Hashimoto, K.; Higashisaka, K.; Haga, Y.; Tsutsumi, Y.; Mizuguchi, K.
    Evidence-Based Prediction of Cellular Toxicity for Amorphous Silica Nanoparticles
    ACS Nano 2023
    https://doi.org/10.1021/acsnano.2c11968
  5. Maruyama, S.; Matsuoka, T.; Hosomi, K.; Park, J.; Nishimura, M.; Murakami, H.; Konishi, K.; Miyachi, M.; Kawashima, H.; Mizuguchi, K.; Kobayashi, T.; Ooka, T.; Yamagata, Z.; Kunisawa, J.
    Characteristic Gut Bacteria in High Barley Consuming Japanese Individuals without Hypertension
    Microorganisms 2023
    https://doi.org/10.3390/microorganisms11051246
  6. Nojima, Y.; Aoki, M.; Re, S.; Hirano, H.; Abe, Y.; Narumi, R.; Muraoka, S.; Shoji, H.; Honda, K.; Tomonaga, T.; Mizuguchi, K.; Boku, N.; Adachi, J.
    Integration of pharmacoproteomic and computational approaches reveals the cellular signal transduction pathways affected by apatinib in gastric cancer cell lines
    Computational and Structural Biotechnology Journal 2023
    https://doi.org/10.1016/j.csbj.2023.03.006
  7. Ikeda, K.; Maezawa, Y.; Yonezawa, T.; Shimizu, Y.; Tashiro, T.; Kanai, S.; Sugaya, N.; Masuda, Y.; Inoue, N.; Niimi, T.; Masuya, K.; Mizuguchi, K.; Furuya, T.; Osawa, M.
    DLiP-PPI library: An integrated chemical database of small-to-medium-sized molecules targeting protein–protein interactions
    Frontiers in Chemistry 2023, 10
    https://doi.org/10.3389/fchem.2022.1090643

2022

  1. Watanabe, R.; Kawata, T.; Ueda, S.; Shinbo, T.; Higashimori, M.; Kitatani, Y.N.; Mizuguchi, K.
    Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information
    Molecular Pharmaceutics 2022
    https://doi.org/10.1021/acs.molpharmaceut.2c00698
  2. Sawane, K.; Hosomi, K.; Park, J.; Ookoshi, K.; Nanri, H.; Nakagata, T.; Chen, Y.A.; Mohsen, A.; Kawashima, H.; Mizuguchi, K.; Miyachi, M.; Kunisawa, J.
    Identification of Human Gut Microbiome Associated with Enterolignan Production
    Microorganisms 2022, 10(11), 2169-2169
    https://doi.org/10.3390/microorganisms10112169
  3. Hosoe, Y.; Miyanoiri, Y.; Re, S.; Ochi, S.; Asahina, Y.; Kawakami, T.; Kuroda, M.; Mizuguchi, K.; Oda, M.
    Structural dynamics of the N‐terminal SH2 domain of PI3K in its free and CD28‐bound states
    The FEBS Journal 2022, 290(9), 2366-2378
    https://doi.org/10.1111/febs.16666
  4. Hosomi, K.; Saito, M.; Park, J.; Murakami, H.; Shibata, N.; Ando, M.; Nagatake, T.; Konishi, K.; Ohno, H.; Tanisawa, K.; Mohsen, A.; Chen, Y.A.; Kawashima, H.; Kitatani, Y.N.; Oka, Y.; Shimizu, H.; Furuta, M.; Tojima, Y.; Sawane, K.; Saika, A.; Kondo, S.; Yonejima, Y.; Takeyama, H.; Matsutani, A.; Mizuguchi, K.; Miyachi, M.; Kunisawa, J.
    Oral administration of Blautia wexlerae ameliorates obesity and type 2 diabetes via metabolic remodeling of the gut microbiota
    Nature Communications 2022, 13(1), 4477-4477
    https://doi.org/10.1038/s41467-022-32015-7
  5. Chen, Y.A.; Osorio, R.S.A.; Mizuguchi, K.
    TargetMine 2022: A new vision into drug target analysis.
    Bioinformatics (Oxford, England) 2022
    https://doi.org/10.1093/bioinformatics/btac507
  6. Gupta, S.; Vundavilli, H.; Osorio, R.S.A.; Itoh, M.N.; Mohsen, A.; Datta, A.; Mizuguchi, K.; Tripathi, L.P.
    Integrative Network Modeling Highlights the Crucial Roles of Rho-GDI Signaling Pathway in the Progression of Non-Small Cell Lung Cancer.
    IEEE journal of biomedical and health informatics 2022, PP
    https://doi.org/10.1109/JBHI.2022.3190038
  7. Mohsen, A.; Chen, Y.A.; Osorio, R.S.A.; Higuchi, C.; Mizuguchi, K.
    Snaq: A Dynamic Snakemake Pipeline for Microbiome Data Analysis With QIIME2
    Frontiers in Bioinformatics 2022, 2893933-893933
    https://doi.org/10.3389/fbinf.2022.893933
  8. Park, J.; Hosomi, K.; Kawashima, H.; Chen, Y.A.; Mohsen, A.; Ohno, H.; Konishi, K.; Tanisawa, K.; Kifushi, M.; Kogawa, M.; Takeyama, H.; Murakami, H.; Kubota, T.; Miyachi, M.; Kunisawa, J.; Mizuguchi, K.
    Dietary Vitamin B1 Intake Influences Gut Microbial Community and the Consequent Production of Short-Chain Fatty Acids.
    Nutrients 2022, 14(10)
    https://doi.org/10.3390/nu14102078
  9. Alarabi, A.B.; Mohsen, A.; Mizuguchi, K.; Alshbool, F.Z.; Khasawneh, F.T.
    Co-expression analysis to identify key modules and hub genes associated with COVID-19 in platelets
    BMC Medical Genomics 2022, 15(1)
    https://doi.org/10.1186/s12920-022-01222-y
  10. Ikubo, Y.; Sanada, T.J.; Hosomi, K.; Park, J.; Naito, A.; Shoji, H.; Misawa, T.; Suda, R.; Sekine, A.; Sugiura, T.; Shigeta, A.; Nanri, H.; Sakao, S.; Tanabe, N.; Mizuguchi, K.; Kunisawa, J.; Suzuki, T.; Tatsumi, K.
    Altered gut microbiota and its association with inflammation in patients with chronic thromboembolic pulmonary hypertension: a single-center observational study in Japan
    BMC Pulmonary Medicine 2022, 22(1)
    https://doi.org/10.1186/s12890-022-01932-0
  11. Kitatani, Y.N.; Itoh, M.N.; Takeda, Y.; Kuroda, M.; Hirata, H.; Miyake, K.; Shiroyama, T.; Shirai, Y.; Noda, Y.; Adachi, Y.; Enomoto, T.; Amiya, S.; Adachi, J.; Narumi, R.; Muraoka, S.; Tomonaga, T.; Kurohashi, S.; Cheng, F.; Tanaka, R.; Yada, S.; Aramaki, E.; Wakamiya, S.; Chen, Y.A.; Higuchi, C.; Nojima, Y.; Fujiwara, T.; Nagao, C.; Matsumura, Y.; Mizuguchi, K.; Kumanogoh, A.; Ueda, N.
    Data-driven patient stratification and drug target discovery by using medical information and serum proteome data of idiopathic pulmonary fibrosis patients
    2022
    https://doi.org/10.21203/rs.3.rs-405195/v2
  12. Maruyama, S.; Matsuoka, T.; Hosomi, K.; Park, J.; Nishimura, M.; Murakami, H.; Konishi, K.; Miyachi, M.; Kawashima, H.; Mizuguchi, K.; Kobayashi, T.; Ooka, T.; Yamagata, Z.; Kunisawa, J.
    Classification of the Occurrence of Dyslipidemia Based on Gut Bacteria Related to Barley Intake
    Frontiers in Nutrition 2022, 9
    https://doi.org/10.3389/fnut.2022.812469
  13. Tsuji, T.; Hashiguchi, K.; Yoshida, M.; Ikeda, T.; Koga, Y.; Honda, Y.; Tanaka, T.; Re, S.; Mizuguchi, K.; Takahashi, D.; Yazaki, R.; Ohshima, T.
    α-Amino acid and peptide synthesis using catalytic cross-dehydrogenative coupling
    Nature Synthesis 2022, 1(4), 304-312
    https://doi.org/10.1038/s44160-022-00037-0
  14. Hirano, H.; Abe, Y.; Nojima, Y.; Aoki, M.; Shoji, H.; Isoyama, J.; Honda, K.; Boku, N.; Mizuguchi, K.; Tomonaga, T.; Adachi, J.
    Temporal dynamics from phosphoproteomics using endoscopic biopsy specimens provides new therapeutic targets in stage IV gastric cancer
    Scientific Reports 2022, 12(1)
    https://doi.org/10.1038/s41598-022-08430-7
  15. Matsuoka, T.; Hosomi, K.; Park, J.; Goto, Y.; Nishimura, M.; Maruyama, S.; Murakami, H.; Konishi, K.; Miyachi, M.; Kawashima, H.; Mizuguchi, K.; Kobayashi, T.; Yokomichi, H.; Kunisawa, J.; Yamagata, Z.
    Relationships between barley consumption and gut microbiome characteristics in a healthy Japanese population: a cross-sectional study
    BMC Nutrition 2022, 8(1), 23-23
    https://doi.org/10.1186/s40795-022-00500-3
  16. Otoshi, T.; Nagano, T.; Park, J.; Hosomi, K.; Yamashita, T.; Tachihara, M.; Tabata, T.; Sekiya, R.; Tanaka, Y.; Kobayashi, K.; Mizuguchi, K.; Itoh, T.; Maniwa, Y.; Kunisawa, J.; Nishimura, Y.
    The Gut Microbiome as a Biomarker of Cancer Progression Among Female Never-smokers With Lung Adenocarcinoma
    Anticancer Research 2022, 42(3), 1589-1598
    https://doi.org/10.21873/anticanres.15633
  17. Miki, Y.; Taketomi, Y.; Kidoguchi, Y.; Yamamoto, K.; Muramatsu, K.; Nishito, Y.; Park, J.; Hosomi, K.; Mizuguchi, K.; Kunisawa, J.; Soga, T.; Boilard, E.; Gowda, S.G.B.; Ikeda, K.; Arita, M.; Murakami, M.
    Group IIA secreted phospholipase A2 controls skin carcinogenesis and psoriasis by shaping the gut microbiota
    JCI Insight 2022, 7(2)
    https://doi.org/10.1172/jci.insight.152611
  18. Yamane, D.; Onitsuka, S.; Re, S.; Isogai, H.; Hamada, R.; Hiramoto, T.; Kawanishi, E.; Mizuguchi, K.; Shindo, N.; Ojida, A.
    Selective covalent targeting of SARS-CoV-2 main protease by enantiopure chlorofluoroacetamide
    Chemical Science 2022, 13(10), 3027-3034
    https://doi.org/10.1039/d1sc06596c
  19. Arakawa, M.; Tabata, K.; Ishida, K.; Kobayashi, M.; Arai, A.; Ishikawa, T.; Suzuki, R.; Takeuchi, H.; Tripathi, L.P.; Mizuguchi, K.; Morita, E.
    Flavivirus recruits the valosin-containing protein (VCP)/NPL4 complex to induce stress granule disassembly for efficient viral genome replication
    Journal of Biological Chemistry 2022, 101597-101597
    https://doi.org/10.1016/j.jbc.2022.101597
  20. Takano, J.; Ito, S.; Dong, Y.; Sharif, J.; Takagi, Y.N.; Umeyama, T.; Han, Y.W.; Isono, K.; Kondo, T.; Iizuka, Y.; Miyai, T.; Koseki, Y.; Ikegaya, M.; Sakihara, M.; Nakayama, M.; Ohara, O.; Hasegawa, Y.; Hashimoto, K.; Arner, E.; Klose, R.J.; Iwama, A.; Koseki, H.; Ikawa, T.
    PCGF1-PRC1 links chromatin repression with DNA replication during hematopoietic cell lineage commitment
    Nature Communications 2022, 13(1), 7159-7159
    https://doi.org/10.1038/s41467-022-34856-8
  21. Pascarella, G.; Hon, C.C.; Hashimoto, K.; Busch, A.; Luginbühl, J.; Parr, C.; Yip, W.H.; Abe, K.; Kratz, A.; Bonetti, A.; Agostini, F.; Severin, J.; Murayama, S.; Suzuki, Y.; Gustincich, S.; Frith, M.; Carninci, P.
    Recombination of repeat elements generates somatic complexity in human genomes.
    Cell 2022, 185(16), 3025-3040
    https://doi.org/10.1016/j.cell.2022.06.032
  22. Vuoristo, S.; Bhagat, S.; Granskog, C.H.; Yoshihara, M.; Gawriyski, L.; Jouhilahti, E.M.; Ranga, V.; Tamirat, M.; Huhtala, M.; Kirjanov, I.; Nykänen, S.; Krjutškov, K.; Damdimopoulos, A.; Weltner, J.; Hashimoto, K.; Recher, G.; Ezer, S.; Paluoja, P.; Paloviita, P.; Takegami, Y.; Kanemaru, A.; Lundin, K.; Airenne, T.T.; Otonkoski, T.; Tapanainen, J.S.; Kawaji, H.; Murakawa, Y.; Bürglin, T.R.; Varjosalo, M.; Johnson, M.S.; Tuuri, T.; Katayama, S.; Kere, J.
    DUX4 is a multifunctional factor priming human embryonic genome activation.
    iScience 2022, 25(4), 104137-104137
    https://doi.org/10.1016/j.isci.2022.104137
  23. Kuroda, M.; Watanabe, R.; Esaki, T.; Kawashima, H.; Ohashi, R.; Sato, T.; Honma, T.; Komura, H.; Mizuguchi, K.
    Utilizing public and private sector data to build better machine learning models for the prediction of pharmacokinetic parameters.
    Drug discovery today 2022, 103339-103339
    https://doi.org/10.1016/j.drudis.2022.103339

2021

  1. Kageyama, S.; Inoue, R.; Hosomi, K.; Park, J.; Yumioka, H.; Suka, T.; Kurohashi, Y.; Teramoto, K.; Syauki, A.Y.; Doi, M.; Sakaue, H.; Mizuguchi, K.; Kunisawa, J.; Irie, Y.
    Effects of Malted Rice Amazake on Constipation Symptoms and Gut Microbiota in Children and Adults with Severe Motor and Intellectual Disabilities: A Pilot Study
    Nutrients 2021, 13(12), 4466-4466
    https://doi.org/10.3390/nu13124466
  2. Park, J.; Kato, K.; Murakami, H.; Hosomi, K.; Tanisawa, K.; Nakagata, T.; Ohno, H.; Konishi, K.; Kawashima, H.; Chen, Y.A.; Mohsen, A.; Xiao, J.Z.; Odamaki, T.; Kunisawa, J.; Mizuguchi, K.; Miyachi, M.
    Comprehensive analysis of gut microbiota of a healthy population and covariates affecting microbial variation in two large Japanese cohorts
    BMC Microbiology 2021, 21(1), 151-151
    https://doi.org/10.1186/s12866-021-02215-0
  3. Ueta, M.; Hosomi, K.; Park, J.; Mizuguchi, K.; Sotozono, C.; Kinoshita, S.; Kunisawa, J.
    Categorization of the Ocular Microbiome in Japanese Stevens–Johnson Syndrome Patients With Severe Ocular Complications
    Frontiers in Cellular and Infection Microbiology 2021, 11741654-741654
    https://doi.org/10.3389/fcimb.2021.741654
  4. Mohsen, A.; Tripathi, L.P.; Mizuguchi, K.
    Deep Learning Prediction of Adverse Drug Reactions in Drug Discovery Using Open TG–GATEs and FAERS Databases
    Frontiers in Drug Discovery 2021, 1
    https://doi.org/10.3389/fddsv.2021.768792
  5. Tomizawa, R.; Park, J.; Matsumoto, N.; Hosomi, K.; Kawashima, H.; Mizuguchi, K.; Kunisawa, J.; Honda, C.
    Relationship between Human Gut Microbiota and Nutrition Intake in Hypertensive Discordant Monozygotic Twins
    Journal of Hypertension: Open Access 2021, 10(8)
    https://www.hilarispublisher.com/open-access/relationship-between-human-gut-microbiota-and-nutrition-intake-in-hypertensive-discordant-monozygotic-twins-73881.html
  6. Kitatani, Y.N.; Mizuguchi, K.; Ueda, N.
    Subset-binding: A novel algorithm to detect paired itemsets from heterogeneous data including biological datasets
    2021
    https://doi.org/10.21203/rs.3.rs-405195/v1
  7. Lee, J.; Mohsen, A.; Banerjee, A.; Mccullough, L.D.; Mizuguchi, K.; Shimaoka, M.; Kiyono, H.; Park, E.J.
    Distinct Age-Specific miRegulome Profiling of Isolated Small and Large Intestinal Epithelial Cells in Mice
    International Journal of Molecular Sciences 2021, 22(7), 3544-3544
    https://doi.org/10.3390/ijms22073544
  8. Matsumoto, N.; Park, J.; Tomizawa, R.; Kawashima, H.; Hosomi, K.; Mizuguchi, K.; Honda, C.; Ozaki, R.; Iwatani, Y.; Watanabe, M.; Kunisawa, J.
    Relationship between Nutrient Intake and Human Gut Microbiota in Monozygotic Twins
    Medicina 2021, 57(3), 275-275
    https://doi.org/10.3390/medicina57030275
  9. Vundavilli, H.; P.tripathi, L.; Datta, A.; Mizuguchi, K.
    Network Modeling and Inference of Peroxisome Proliferator-Activated Receptor Pathway in High fat diet-linked Obesity.
    Journal of theoretical biology 2021, 110647-110647
    https://doi.org/10.1016/j.jtbi.2021.110647
  10. Watanabe, R.; Esaki, T.; Ohashi, R.; Kuroda, M.; Kawashima, H.; Komura, H.; Kitatani, Y.N.; Mizuguchi, K.
    Development of an In Silico Prediction Model for P-glycoprotein Efflux Potential in Brain Capillary Endothelial Cells toward the Prediction of Brain Penetration
    Journal of Medicinal Chemistry 2021
    https://doi.org/10.1021/acs.jmedchem.0c02011
  11. Re, S.; Mizuguchi, K.
    Glycan Cluster Shielding and Antibody Epitopes on Lassa Virus Envelop Protein
    The Journal of Physical Chemistry B 2021, 125(8), 2089-2097
    https://doi.org/10.1021/acs.jpcb.0c11516
  12. Koba, T.; Takeda, Y.; Narumi, R.; Shiromizu, T.; Nojima, Y.; Ito, M.; Kuroyama, M.; Futami, Y.; Takimoto, T.; Matsuki, T.; Edahiro, R.; Nojima, S.; Hayama, Y.; Fukushima, K.; Hirata, H.; Koyama, S.; Iwahori, K.; Nagatomo, I.; Suzuki, M.; Shirai, Y.; Murakami, T.; Nakanishi, K.; Nakatani, T.; Suga, Y.; Miyake, K.; Shiroyama, T.; Kida, H.; Sasaki, T.; Ueda, K.; Mizuguchi, K.; Adachi, J.; Tomonaga, T.; Kumanogoh, A.
    Proteomics of serum extracellular vesicles identifies a novel COPD biomarker, fibulin-3 from elastic fibres
    ERJ Open Research 2021, 7(1), 00658-2020
    https://doi.org/10.1183/23120541.00658-2020
  13. Hashimoto, K.; Jouhilahti, E.M.; Töhönen, V.; Carninci, P.; Kere, J.; Katayama, S.
    Embryonic LTR retrotransposons supply promoter modules to somatic tissues
    Genome Research 2021, 31(11), 1983-1993
    https://doi.org/10.1101/gr.275354.121
  14. Abugessaisa, I.; Ramilowski, J.A.; Lizio, M.; Severin, J.; Hasegawa, A.; Harshbarger, J.; Kondo, A.; Noguchi, S.; Yip, C.W.; Ooi, J.L.C.; Tagami, M.; Hori, F.; Agrawal, S.; Hon, C.C.; Cardon, M.; Ikeda, S.; Ono, H.; Bono, H.; Kato, M.; Hashimoto, K.; Bonetti, A.; Kato, M.; Kobayashi, N.; Shin, J.; Hoon, M.D.; Hayashizaki, Y.; Carninci, P.; Kawaji, H.; Kasukawa, T.
    FANTOM enters 20th year: expansion of transcriptomic atlases and functional annotation of non-coding RNAs.
    Nucleic acids research 2021, 49(D1), D892-D898
    https://doi.org/10.1093/nar/gkaa1054

2020

  1. Tripathi, L.P.; Itoh, M.N.; Takeda, Y.; Tsujino, K.; Kondo, Y.; Kumanogoh, A.; Mizuguchi, K.
    Integrative Analysis Reveals Common and Unique Roles of Tetraspanins in Fibrosis and Emphysema
    Frontiers in Genetics 2020, 11585998-585998
    https://doi.org/10.3389/fgene.2020.585998
  2. Chen, Y.A.; Park, J.; Kitatani, Y.N.; Kawashima, H.; Mohsen, A.; Hosomi, K.; Tanisawa, K.; Ohno, H.; Konishi, K.; Murakami, H.; Miyachi, M.; Kunisawa, J.; Mizuguchi, K.
    MANTA, an integrative database and analysis platform that relates microbiome and phenotypic data
    PLOS ONE 2020, 15(12), e0243609-e0243609
    https://doi.org/10.1371/journal.pone.0243609
  3. Afanasyeva, A.; Nagao, C.; Mizuguchi, K.
    Developing a Kinase-Specific Target Selection Method Using a Structure-Based Machine Learning Approach
    Advances and Applications in Bioinformatics and Chemistry 2020, Volume 1327-40
    https://doi.org/10.2147/aabc.s278900
  4. Nojima, Y.; Takeda, Y.; Maeda, Y.; Bamba, T.; Fukusaki, E.; Itoh, M.N.; Mizuguchi, K.; Kumanogoh, A.
    Metabolomic analysis of fibrotic mice combined with public RNA-Seq human lung data reveal potential diagnostic biomarker candidates for lung fibrosis.
    FEBS open bio 2020, 10(11), 2427-2436
    https://doi.org/10.1002/2211-5463.12982
  5. Osorio, R.S.A.; Persson, J.T.N.; Nojima, Y.; Kosugi, Y.; Mizuguchi, K.; Kitatani, Y.N.
    Panomicon: A web-based environment for interactive, visual analysis of multi-omics data.
    Heliyon 2020, 6(8), e04618-e04618
    https://doi.org/10.1016/j.heliyon.2020.e04618
  6. Saito, A.; Tsuchiya, D.; Sato, S.; Okamoto, A.; Murakami, Y.; Mizuguchi, K.; Toh, H.; Nemoto, W.
    Update of the GRIP web service.
    Journal of receptor and signal transduction research 2020, 40(4), 348-356
    https://doi.org/10.1080/10799893.2020.1734821
  7. Tabata, T.; Yamashita, T.; Hosomi, K.; Park, J.; Hayashi, T.; Yoshida, N.; Saito, Y.; Fukuzawa, K.; Konishi, K.; Murakami, H.; Kawashima, H.; Mizuguchi, K.; Miyachi, M.; Kunisawa, J.; Hirata, K.I.
    Gut microbial composition in patients with atrial fibrillation: effects of diet and drugs.
    Heart and vessels 2020, 36(1), 105-114
    https://doi.org/10.1007/s00380-020-01669-y
  8. Sanada, T.J.; Hosomi, K.; Shoji, H.; Park, J.; Naito, A.; Ikubo, Y.; Yanagisawa, A.; Kobayashi, T.; Miwa, H.; Suda, R.; Sakao, S.; Mizuguchi, K.; Kunisawa, J.; Tanabe, N.; Tatsumi, K.
    Gut microbiota modification suppresses the development of pulmonary arterial hypertension in an SU5416/hypoxia rat model
    Pulmonary Circulation 2020, 10(3), 204589402092914-204589402092914
    https://doi.org/10.1177/2045894020929147
  9. Tokunaga, M.; Miyamoto, Y.; Suzuki, T.; Otani, M.; Inuki, S.; Esaki, T.; Nagao, C.; Mizuguchi, K.; Ohno, H.; Yoneda, Y.; Okamoto, T.; Oka, M.; Matsuura, Y.
    Novel anti-flavivirus drugs targeting the nucleolar distribution of core protein.
    Virology 2020, 54141-51
    https://doi.org/10.1016/j.virol.2019.11.015
  10. Kajihara, D.; Hon, C.C.; Abdullah, A.N.; Jr, J.W.; Moretti, A.I.S.; Poloni, J.F.; Bonatto, D.; Hashimoto, K.; Carninci, P.; Laurindo, F.R.M.
    Analysis of splice variants of the human protein disulfide isomerase (P4HB) gene.
    BMC genomics 2020, 21(1), 766-766
    https://doi.org/10.1186/s12864-020-07164-y
  11. Taguchi, A.; Nagasaka, K.; Plessy, C.; Nakamura, H.; Kawata, Y.; Kato, S.; Hashimoto, K.; Nagamatsu, T.; Oda, K.; Kukimoto, I.; Kawana, K.; Carninci, P.; Osuga, Y.; Fujii, T.
    Use of Cap Analysis Gene Expression to detect human papillomavirus promoter activity patterns at different disease stages.
    Scientific reports 2020, 10(1), 17991-17991
    https://doi.org/10.1038/s41598-020-75133-2
  12. Ramilowski, J.A.; Yip, C.W.; Agrawal, S.; Chang, J.C.; Ciani, Y.; Kulakovskiy, I.V.; Mendez, M.; Ooi, J.L.C.; Ouyang, J.F.; Parkinson, N.; Petri, A.; Roos, L.; Severin, J.; Yasuzawa, K.; Abugessaisa, I.; Akalin, A.; Antonov, I.V.; Arner, E.; Bonetti, A.; Bono, H.; Borsari, B.; Brombacher, F.; Cameron, C.J.; Cannistraci, C.V.; Cardenas, R.; Cardon, M.; Chang, H.; Dostie, J.; Ducoli, L.; Favorov, A.; Fort, A.; Garrido, D.; Gil, N.; Gimenez, J.; Guler, R.; Handoko, L.; Harshbarger, J.; Hasegawa, A.; Hasegawa, Y.; Hashimoto, K.; Hayatsu, N.; Heutink, P.; Hirose, T.; Imada, E.L.; Itoh, M.; Kaczkowski, B.; Kanhere, A.; Kawabata, E.; Kawaji, H.; Kawashima, T.; Kelly, S.T.; Kojima, M.; Kondo, N.; Koseki, H.; Kouno, T.; Kratz, A.; Stolarska, M.K.; Kwon, A.T.J.; Leek, J.; Lennartsson, A.; Lizio, M.; Redondo, F.L.; Luginbühl, J.; Maeda, S.; Makeev, V.J.; Marchionni, L.; Medvedeva, Y.A.; Minoda, A.; Müller, F.; Aguirre, M.M.; Murata, M.; Nishiyori, H.; Nitta, K.R.; Noguchi, S.; Noro, Y.; Nurtdinov, R.; Okazaki, Y.; Orlando, V.; Paquette, D.; Parr, C.J.C.; Rackham, O.J.L.; Rizzu, P.; Martinez, D.F.S.; Sandelin, A.; Sanjana, P.; Semple, C.A.M.; Shibayama, Y.; Sivaraman, D.M.; Suzuki, T.; Szumowski, S.C.; Tagami, M.; Taylor, M.S.; Terao, C.; Thodberg, M.; Thongjuea, S.; Tripathi, V.; Ulitsky, I.; Verardo, R.; Vorontsov, I.E.; Yamamoto, C.; Young, R.S.; Baillie, J.K.; Forrest, A.R.R.; Guigó, R.; Hoffman, M.M.; Hon, C.C.; Kasukawa, T.; Kauppinen, S.; Kere, J.; Lenhard, B.; Schneider, C.; Suzuki, H.; Yagi, K.; Hoon, M.J.L.D.; Shin, J.W.; Carninci, P.
    Functional annotation of human long noncoding RNAs via molecular phenotyping.
    Genome research 2020, 30(7), 1060-1072
    https://doi.org/10.1101/gr.254219.119
  13. Bonetti, A.; Agostini, F.; Suzuki, A.M.; Hashimoto, K.; Pascarella, G.; Gimenez, J.; Roos, L.; Nash, A.J.; Ghilotti, M.; Cameron, C.J.F.; Valentine, M.; Medvedeva, Y.A.; Noguchi, S.; Agirre, E.; Kashi, K.; Samudyata, ; Luginbühl, J.; Cazzoli, R.; Agrawal, S.; Luscombe, N.M.; Blanchette, M.; Kasukawa, T.; Hoon, M.D.; Arner, E.; Lenhard, B.; Plessy, C.; Branco, G.C.; Orlando, V.; Carninci, P.
    RADICL-seq identifies general and cell type-specific principles of genome-wide RNA-chromatin interactions.
    Nature communications 2020, 11(1), 1018-1018
    https://doi.org/10.1038/s41467-020-14337-6
  14. Komura, H.; Watanabe, R.; Kawashima, H.; Ohashi, R.; Kuroda, M.; Sato, T.; Honma, T.; Mizuguchi, K.
    A public–private partnership to enrich the development of in silico predictive models for pharmacokinetic and cardiotoxic properties
    Drug Discovery Today 2020, 26(5), 1275-1283
    https://doi.org/10.1016/j.drudis.2021.01.024
  15. Esaki, T.; Kumazawa, K.; Takahashi, K.; Watanabe, R.; Masuda, T.; Watanabe, H.; Shimizu, Y.; Okada, A.; Takimoto, S.; Shimada, T.; Ikeda, K.
    Open Innovation Platform using Cloud-based Applications and Collaborative Space: A Case Study of Solubility Prediction Model Development
    Chem-Bio Informatics Journal 2020, 20(0), 5-18
    https://doi.org/10.1273/cbij.20.5
  16. Afanasyeva, A.; Nagao, C.; Mizuguchi, K.
    Developing a Kinase-Specific Target Selection Method Using a Structure-Based Machine Learning Approach
    Advances and Applications in Bioinformatics and Chemistry 2020, Volume 1327-40
    https://doi.org/10.2147/AABC.S278900

2019

  1. Mohsen, A.; Park, J.; Chen, Y.A.; Kawashima, H.; Mizuguchi, K.
    Impact of quality trimming on the efficiency of reads joining and diversity analysis of Illumina paired-end reads in the context of QIIME1 and QIIME2 microbiome analysis frameworks
    BMC Bioinformatics 2019, 20(1), 581
    https://doi.org/10.1186/s12859-019-3187-5
  2. Allendes, R.S.; Tripathi, L.P.; Mizuguchi, K.
    CLINE: a web-tool for the comparison of biological dendrogram structures
    BMC Bioinformatics 2019, 20(1), 528
    https://doi.org/10.1186/s12859-019-3149-y
  3. Miyake, K.; Sakane, A.; Tsuchiya, Y.; Sagawa, I.; Tomida, Y.; Kasahara, J.; Imoto, I.; Watanabe, S.; Higo, D.; Mizuguchi, K.; Sasaki, T.
    Actin Cytoskeletal Reorganization Function of JRAB/MICAL-L2 Is Fine-tuned by Intramolecular Interaction between First LIM Zinc Finger and C-terminal Coiled-coil Domains
    Scientific Reports 2019, 9(1), 12794-12794
    https://doi.org/10.1038/s41598-019-49232-8
  4. Chiba, S.; Ohue, M.; Gryniukova, A.; Borysko, P.; Zozulya, S.; Yasuo, N.; Yoshino, R.; Ikeda, K.; Shin, W.H.; Kihara, D.; Iwadate, M.; Umeyama, H.; Ichikawa, T.; Teramoto, R.; Hsin, K.Y.; Gupta, V.; Kitano, H.; Sakamoto, M.; Higuchi, A.; Miura, N.; Yura, K.; Mochizuki, M.; Ramakrishnan, C.; Thangakani, A.M.; Velmurugan, D.; Gromiha, M.M.; Nakane, I.; Uchida, N.; Hakariya, H.; Tan, M.; Nakamura, H.K.; Suzuki, S.D.; Ito, T.; Kawatani, M.; Kudoh, K.; Takashina, S.; Yamamoto, K.Z.; Moriwaki, Y.; Oda, K.; Kobayashi, D.; Okuno, T.; Minami, S.; Chikenji, G.; Prathipati, P.; Nagao, C.; Mohsen, A.; Ito, M.; Mizuguchi, K.; Honma, T.; Ishida, T.; Hirokawa, T.; Akiyama, Y.; Sekijima, M.
    A prospective compound screening contest identified broader inhibitors for Sirtuin 1
    Scientific Reports 2019, 9(1), 19585
    https://doi.org/10.1038/s41598-019-55069-y
  5. Watanabe, R.; Ohashi, R.; Esaki, T.; Kawashima, H.; Natsume, Y.; Nagao, C.; Mizuguchi, K.
    Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor
    Scientific Reports 2019, 9(1), 18782-18782
    https://doi.org/10.1038/s41598-019-55325-1
  6. Afanasyeva, A.; Nagao, C.; Mizuguchi, K.
    Prediction of the secondary structure of short DNA aptamers
    Biophysics and Physicobiology 2019, 16(0), 287-294
    https://doi.org/10.2142/biophysico.16.0_287
  7. R§, W.; R§, O.; Esaki, T.; Taniguchi, T.; Torimoto, N.; Watanabe, T.; Ogasawara, Y.; Takahashi, T.; Tsukimoto, M.; Mizuguchi, K.
    Development of Simplified in Vitro P-Glycoprotein Substrate Assay and in Silico Prediction Models To Evaluate Transport Potential of P-Glycoprotein
    Mol. Pharmaceutics 2019, 16(5), 1851-1863

Reviews

2022

  1. Murakami, Y.; Mizuguchi, K.
    Recent developments of sequence-based prediction of protein-protein interactions.
    Biophysical reviews 2022, 1-19
    https://doi.org/10.1007/s12551-022-01038-1
  2. Kuroda, M.; Watanabe, R.; Esaki, T.; Kawashima, H.; Ohashi, R.; Sato, T.; Honma, T.; Komura, H.; Mizuguchi, K.
    Utilizing public and private sector data to build better machine learning models for the prediction of pharmacokinetic parameters.
    Drug discovery today 2022, 103339-103339
    https://doi.org/10.1016/j.drudis.2022.103339

2021

  1. Komura, H.; Watanabe, R.; Kawashima, H.; Ohashi, R.; Kuroda, M.; Sato, T.; Honma, T.; Mizuguchi, K.
    A public–private partnership to enrich the development of in silico predictive models for pharmacokinetic and cardiotoxic properties
    Drug Discovery Today 2021
    https://doi.org/10.1016/j.drudis.2021.01.024

2019

  1. Chen, Y.A.; Tripathi, L.P.; Mizuguchi, K.
    Data Warehousing with TargetMine for Omics Data Analysis
    Methods in Molecular Biology 2019, 35-64
    https://doi.org/10.1007/978-1-4939-9442-7_3
  2. Tripathi, L.P.; Chen, Y.A.; Mizuguchi, K.; Morita, E.
    Network-Based Analysis of Host-Pathogen Interactions
    Encyclopedia of Bioinformatics and Computational Biology 2019, 932
    https://doi.org/10.1016/b978-0-12-809633-8.20170-2
  3. Tripathi, L.P.; Chen, Y.A.; Mizuguchi, K.; Murakami, Y.
    Network-Based Analysis for Biological Discovery
    Encyclopedia of Bioinformatics and Computational Biology 2019, 283
    https://doi.org/10.1016/b978-0-12-809633-8.20674-2
  4. Tripathi, L.P.; Esaki, T.; Itoh, M.N.; Chen, Y.A.; Mizuguchi, K.
    Integrative Analysis of Multi-Omics Data
    Encyclopedia of Bioinformatics and Computational Biology 2019, 194
    https://doi.org/10.1016/b978-0-12-809633-8.20096-4

Books

2020

  1. Afanasyeva, A.; Nagao, C.; Mizuguchi, K.
    Protein Interactions: Computational Methods, Analysis and Applications
    World Scientific 2020 (DOI: 10.1142/11596)
    https://doi.org/10.1142/11596

論文

原著論文

2023

  1. 長尾 知生子, 鎌田 真由美, 中津井 雅彦, 深川 明子, 片山 俊明, 川島 秀一, 水口 賢司, 安倍 理加
    医薬品関連文書の利活用に向けたインタビューフォームの構造化の提案
    医薬品情報学 2023, 24(4), 187-195
    https://doi.org/10.11256/jjdi.24.187

総説

2023

  1. 橋本 浩介, 新井 康通
    百寿者にみる老化後期におけるリンパ球の変化
    実験医学 2023, 41(8), 1276-1279
    https://doi.org/10.18958/7239-00001-0000457-00

2022

  1. 中村 恵宣, 北村 英也, 小倉 髙志, 夏目 やよい, 水口 賢司
    官民研究開発投資拡大プログラム（PRISM）で構築する特発性肺線維症に対する創薬標的探索プラットフォームについて
    MEDCHEM NEWS 2022, 32(3), 119-123
    https://doi.org/10.14894/medchem.32.3_119
  2. 橋本浩介, 新井康通
    加速する1細胞レベルのT細胞レセプター解析-百寿者研究への応用-
    月刊臨床免疫・アレルギー科 2022, 78(2), 194-198
    https://jglobal.jst.go.jp/detail?JGLOBAL_ID=202202285768942279
  3. 橋本浩介
    トランスクリプトームデータの一般的な解析手順
    医学のあゆみ 2022, 280(12), 1267-1272

2021

  1. 陳 怡安, 李 秀栄, 水口 賢司
    TargetMineによる生物学的知識の発見 (第1土曜特集 構造生命科学による創薬への挑戦) -- (計算機から創薬へ)
    医学のあゆみ 2021, 278(6), 641-645
    http://ci.nii.ac.jp/naid/40022640974
  2. 橋本浩介, 新井康通
    百寿者免疫細胞の1細胞トランスクリプトーム解析
    医学のあゆみ 2021, 276(10), 998-1002
    https://jglobal.jst.go.jp/detail?JGLOBAL_ID=202102229430192665

2020

  1. 渡邉 怜子, 水口 賢司
    人工知能(AI)を用いた創薬プロセスの加速におけるデータの重要性 (第5土曜特集 AIが切り拓く未来の医療) -- (AI技術の創薬への応用)
    医学のあゆみ 2020, 274(9), 838-842
    http://ci.nii.ac.jp/naid/40022324961
  2. 夏目 やよい, 水口 賢司
    【人工知能(AI)技術のヘルスケア利活用】新薬創出を加速するAIの開発
    Precision Medicine 2020, 3(5), 410-413
  3. 長尾 知生子, 水口 賢司
    【イメージング時代の構造生命科学 細胞の動態、膜のないオルガネラ、分子の構造変化をトランススケールに観る】(第4章)活用可能なデータベースとプラットフォーム 対象タンパク質を理解するための有用なデータベース
    実験医学 2020, 38(5), 897-901
    https://search.jamas.or.jp/index.php?module=Default&action=Link&pub_year=2020&ichushi_jid=J01704&link_issn=&doc_id=20200325180034&doc_link_id=%2Fai4jigkb%2F2020%2F003805%2F038%2F
    0897b0901%26dl%3D3&url=http%3A%2F%2Fwww.medicalonline.jp%2Fjamas.php%3FGoodsID%3D%2Fai4jigkb%2F2020%2F003805%2F038%2F0897b0901%26dl%
    3D3&type=MedicalOnline&icon=https%3A%2F%2Fjk04.jamas.or.jp%2Ficon%2F00004_4.gif
  4. 橋本浩介
    一細胞トランスクリプトーム解析の現況
    月刊臨床免疫・アレルギー科 2020, 74(1), 93-96
    https://jglobal.jst.go.jp/detail?JGLOBAL_ID=202002260551204881
  5. 渡邉 怜子, 水口 賢司
    人工知能(AI)を用いた創薬プロセスの加速におけるデータの重要性 (第5土曜特集 AIが切り拓く未来の医療) -- (AI技術の創薬への応用)
    医学のあゆみ 2020, 274(9), 838-842
    http://ci.nii.ac.jp/naid/40022324961

書籍

2023

  1. 陳 怡安, 朴 鐘旭, 水口賢司
    健康と疾患を制御する精密栄養学 : 「何を、いつ、どう食べるか?」に、食品機能の解析と個人差を生む分子メカニズムの解明から迫る
    羊土社 2023 (ISBN: 9784758104111)
    http://ci.nii.ac.jp/ncid/BD02380087
  2. 村上洋一, 長尾知生子, 水口賢司
    ケモインフォマティクスにおけるデータ収集の最適化と解析手法
    技術情報協会 2023 (ISBN: 9784861049446)
    http://ci.nii.ac.jp/ncid/BD02068623

2022

  1. 夏目やよい, 水口賢司
    革新的AI創薬 : 医療ビッグデータ、人工知能がもたらす創薬研究の未来像
    エヌ・ティー・エス 2022 (ISBN: 9784860437886)
    http://ci.nii.ac.jp/ncid/BC15983667
  2. 池田和由, 米澤朋起, 渡邉怜子, 渡邉博文, 高橋一敏, 半田佑磨, 増田友秀, 朴鐘旭, 櫻井研吾, 熊澤啓子, 江崎剛史
    特別企画「気になるツールを使ってみよう（第１回）」
    CBI学会誌編集委員会 2022

2021

  1. 長尾知生子, 李 秀栄, 水口賢司
    創薬研究のための相互作用解析パーフェクト : 低中分子・抗体創薬におけるスクリーニング戦略と実例、in silico解析、一歩進んだ分析技術まで
    羊土社 2021 (ISBN: 9784758122566)
    http://ci.nii.ac.jp/ncid/BC11391757
  2. 渡邉怜子
    ホットトピックス「自動化された薬物動態予測ワークフロー：創薬・開発プロセスへの適用」
    CBI学会誌編集委員会 2021
  3. 長尾知生子, 李秀栄, 水口賢司
    創薬研究のための相互作用解析パーフェクト〜低中分子・抗体創薬におけるスクリーニング戦略と実例、in silico解析、一歩進んだ分析技術まで (実験医学別冊)
    羊土社 2021 (ISBN: 4758122563)
    http://ci.nii.ac.jp/ncid/BC11391757

2020

  1. 渡邉怜子, 水口賢司
    人工知能（AI）を用いた創薬プロセスの加速におけるデータの重要性
    医歯薬出版株式会社 2020
  2. 長尾 知生子, 水口 賢司
    実験医学増刊 Vol.38 No.5 イメージング時代の構造生命科学〜細胞の動態、膜のないオルガネラ、分子の構造変化をトランススケールに観る
    羊土社 2020 (ISBN: 4758103852)
    http://ci.nii.ac.jp/ncid/BB29906634

2019

  1. 水口賢司
    IT・ビッグデータと薬学 : 創薬・医薬品適正使用への活用
    日本学術協力財団 2019
    http://ci.nii.ac.jp/ncid/BB27803696

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