Publications

Original articles

2024

  1. Tomoto M., Mineharu Y., Sato N., Tamada Y., Itoh M.N., Kuroda M., Adachi J., Takeda Y., Mizuguchi K., Kumanogoh A., Kitatani Y.N., Okuno Y.
    Idiopathic pulmonary fibrosis-specific Bayesian network integrating extracellular vesicle proteome and clinical information
    Scientific Reports 2024, 14(1)
    https://doi.org/10.1038/s41598-023-50905-8

2023

  1. Gou Y., Re S., Mizuguchi K., Nagao C.
    Impact of Hydrogen Bonding on P-Glycoprotein Efflux Transport as Revealed by Evaluation of a De Novo Prediction Model
    ACS Medicinal Chemistry Letters 2023
    https://doi.org/10.1021/acsmedchemlett.3c00376
  2. Koyama K., Hashimoto K., Nagao C., Mizuguchi K.
    Attention network for predicting T-cell receptor–peptide binding can associate attention with interpretable protein structural properties
    Frontiers in Bioinformatics 2023, 3
    https://doi.org/10.3389/fbinf.2023.1274599
  3. Iwasaka C., Nanri H., Nakagata T., Ohno H., Tanisawa K., Konishi K., Murakami H., Hosomi K., Park J., Yamada Y., Ono R., Mizuguchi K., Kunisawa J., Miyachi M.
    Association of skeletal muscle function, quantity, and quality with gut microbiota in Japanese adults: A cross-sectional study.
    Geriatrics & gerontology international 2023
    https://doi.org/10.1111/ggi.14751
  4. Yoshimura E., Hamada Y., Hatamoto Y., Nakagata T., Nanri H., Nakayama Y., Hayashi T., Suzuki I., Ando T., Ishikawa‐takata K., Tanaka S., Ono R., Park J., Hosomi K., Mizuguchi K., Kunisawa J., Miyachi M.
    Effects of energy loads on energy and nutrient absorption rates and gut microbiome in humans: A randomized crossover trial
    Obesity 2023
    https://doi.org/10.1002/oby.23935
  5. Kageyama S., Inoue R., Park J., Hosomi K., Yumioka H., Suka T., Teramoto K., Syauki A.Y., Doi M., Sakaue H., Miyake M., Mizuguchi K., Kunisawa J., Irie Y.
    Changes in Fecal Gut Microbiome of Home Healthcare Patients with Disabilities through Consumption of Malted Rice Amazake
    Physiological Genomics 2023
    https://doi.org/10.1152/physiolgenomics.00062.2023
  6. Park J., Bushita H., Nakano A., Hara A., Ueno H.M., Ozato N., Hosomi K., Kawashima H., Chen Y.A., Mohsen A., Ohno H., Konishi K., Tanisawa K., Nanri H., Murakami H., Miyachi M., Kunisawa J., Mizuguchi K., Araki M.
    Ramen Consumption and Gut Microbiota Diversity in Japanese Women: Cross-Sectional Data from the NEXIS Cohort Study
    Microorganisms 2023, 11(8), 1892-1892
    https://doi.org/10.3390/microorganisms11081892
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. Komura H., Watanabe R., Mizuguchi K.
    The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery
    Pharmaceutics 2023, 15(11), 2619-2619
    https://doi.org/10.3390/pharmaceutics15112619

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

2023

  1. Komura H., Watanabe R., Mizuguchi K.
    The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery
    Pharmaceutics 2023, 15(11), 2619-2619
    https://doi.org/10.3390/pharmaceutics15112619

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. MARTIN, 渡邉怜子, 水口賢司
    ナノ粒子をより安全に 設計するための新手法 ──ナノ粒子の安全性に革命を： AI が拓く未来
    化学 2023, 78(11), 27-30
  2. 伊藤 眞里, 武田 吉人, 黒田 正孝, 荒牧 英治, 黒橋 禎夫, 武田 理宏, 梁川 雅弘, 富山 憲幸, 松村 泰志, 足立 淳, 水口 賢司, 上田 修功, 熊ノ郷 淳, 夏目 やよい
    「新薬創出を加速する人工知能の開発」 IPF患者臨床データから創薬標的への知識処理
    日本呼吸器学会誌 2023, 12(増刊), 293-293
    https://search.jamas.or.jp/default/link?pub_year=2023&ichushi_jid=J05953&link_issn=&doc_id=20230602311042&doc_link_id=%2Fci6respo%2F2023%2Fv012s1%2F118%2F0293-0293%26dl%3D0&url=https%3A%2F%2Fwww.medicalonline.jp%2Fjamas.php%3FGoodsID%3D%2Fci6respo%2F2023%2Fv012s1%2F118%2F0293-0293%26dl%3D0&type=MedicalOnline&icon=https%3A%2F%2Fjk04.jamas.or.jp%2Ficon%2F00004_2.gif
  3. 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
  4. Martin, 渡邉怜子, 水口賢司
    ナノ粒子をより安全に設計するための新手法～ナノ粒子の安全性に革命を： AIが拓く未来
    化学 2023, 78(11), 27-30

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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