Personnel Information

写真a

HAYASHI SHUTO


Job title

Associate Professor
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Graduating School 【 display / non-display 】

  • The University of Tokyo, Faculty of Science, Department of Information Science, 2014.03, Graduated

  • The University of Tokyo, The Graduate School of Information Science and Technology, Department of Computer Science Master Course, 2016.03, Graduated

  • The University of Tokyo, The Graduate School of Information Science and Technology, Department of Computer Science Doctoral Course, 2019.03, Graduated

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Campus Career 【 display / non-display 】

  • 2023.01
    -
    2023.03
    Tokyo Medical and Dental University， Medical Research Institute， Division of Biological Data Science， Department of Computational and Systems Biology， Associate Professor
  • 2023.04
    -
    Now
    Tokyo Medical and Dental University， Medical Research Institute， Division of Biological Data Science， Department of Computational and Systems Biology， Associate Professor
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External Career 【 display / non-display 】

  • 2019.04
    -
    2021.03
    The University of Tokyo, Department of Preventive Medicine, Graduate School of Medicine, Research Associate
  • 2021.04
    -
    2022.12
    Nagoya University, Division of Systems Biology, Graduate School of Medicine, Designated Associate Professor
  • 2023.01
    -
    Now
    Tokyo Medical and Dental University, Department of Computational and Systems Biology, Medical Research Institute, Project Associate Professor
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Academic Society Affiliations 【 display / non-display 】

  • The Japanese Society for Hygiene

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Research Areas 【 display / non-display 】

  • Life, health and medical informatics

  • System genome science

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Published Papers & Misc 【 display / non-display 】

  1. Spatial and single-cell transcriptomics decipher the cellular environment containing HLA-G+ cancer cells and SPP1+ macrophages in colorectal cancer. 2023.01; 42 (1): 111929. ( PubMed, DOI )

  2. Bayesian statistical method for detecting structural and topological diversity in polymorphic proteins. 2022.11; 20 6519-6525. ( PubMed, DOI )

  3. Nucleic Acid-triggered Tumoral Immunity Propagates pH-selective Therapeutic Antibodies through Tumor-driven Epitope Spreading. 2022.09; 114 (1): 321-338. ( PubMed, DOI )

  4. Immunogenomic pan-cancer landscape reveals immune escape mechanisms and immunoediting histories 2021.12; 11 (1): 15713. ( PubMed, DOI )

  5. Neoantimon: A multifunctional R package for identification of tumor-specific neoantigens 2020.09; 36 (18): 4813-4816. ( PubMed, DOI )

  6. Classification of primary liver cancer with immunosuppression mechanisms and correlation with genomic alterations 2020.03; 53 102659. ( PubMed, DOI )

  7. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns 2020.02; 11 (1): ( DOI )

  8. Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig 2020.02; 11 (1): ( DOI )

  9. Pathway and network analysis of more than 2500 whole cancer genomes 2020.02; 11 (1): ( DOI )

  10. Integrative pathway enrichment analysis of multivariate omics data 2020.02; 11 (1): ( DOI )

  11. Genomic footprints of activated telomere maintenance mechanisms in cancer 2020.02; 11 (1): ( DOI )

  12. Combined burden and functional impact tests for cancer driver discovery using DriverPower 2020.02; 11 (1): ( DOI )

  13. Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis 2020.02; 3 (1): ( DOI )

  14. Pan-cancer analysis of whole genomes 2020.02; 578 (7793): 82-93. ( PubMed, DOI )

  15. Divergent mutational processes distinguish hypoxic and normoxic tumours 2020.02; 11 (1): ( DOI )

  16. High-coverage whole-genome analysis of 1220 cancers reveals hundreds of genes deregulated by rearrangement-mediated cis-regulatory alterations 2020.02; 11 (1): ( DOI )

  17. Inferring structural variant cancer cell fraction 2020.02; 11 (1): ( DOI )

  18. A Bayesian model integration for mutation calling through data partitioning 2019.11; 35 (21): 4247-4254. ( PubMed, DOI )

  19. Alphlard-nt: Bayesian method for human leukocyte antigen genotyping and mutation calling through simultaneous analysis of normal and tumor whole-genome sequence data 2019.09; 26 (9): 923-937. ( PubMed, DOI )

  20. Quantifying immune-based counterselection of somatic mutations 2019.07; 15 (7): e1008227. ( PubMed, DOI )

  21. A temporal shift of the evolutionary principle shaping intratumor heterogeneity in colorectal cancer 2018.12; 9 (1): 2884. ( PubMed, DOI )

  22. ALPHLARD: A Bayesian method for analyzing HLA genes from whole genome sequence data 2018.11; 19 (1): 790. ( PubMed, DOI )

  23. 肝臓がんにおける免疫抑制機構のゲノム解析(Genomic insights into immune suppression in liver cancer) 2018.09; 77回 906. ( ichushi )

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Conference Activities & Talks 【 display / non-display 】

  1. Whole genome sequence-based analysis of HLA genes with Bayesian statistical model. 2017.07.26

  2. Statistical method for comprehensive analysis of HLA class I and II genes.. 2016.08.09

  3. Bayesian Method for HLA Genotyping from Whole Genome Sequencing Data. 2015.07.21

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