Personnel Information

写真a

HAYASHI Shuto


Job title

Associate Professor

Field Introduction URL

https://www.shimamlab.info/

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Graduating School 【 display / non-display 】

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

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Graduate School 【 display / non-display 】

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

  • The University of Tokyo, Graduate School of Information Science and Technology, Department of Computer Science, Doctor's Course, 2019.03, Completed

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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, Graduate School of Medicine, Assistant Professor
  • 2021.04
    -
    2022.12
    Nagoya University, Graduate School of Medicine, Project Associate Professor
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Academic Society Affiliations 【 display / non-display 】

  • International Society for Computational Biology

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Research Areas 【 display / non-display 】

  • Life, health and medical informatics

  • System genome science

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Research Theme 【 display / non-display 】

  • Development of a fast molecular dynamics simulation method using deep learning, 2022.04 - 2025.03

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Published Papers & Misc 【 display / non-display 】

  1. DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates 2024.09; 25 (1): 229. ( DOI )

  2. Spatial and single-cell colocalisation analysis reveals MDK-mediated immunosuppressive environment with regulatory T cells in colorectal carcinogenesis 2024.05; 103 105102. ( DOI )

  3. Single-cell colocalization analysis using a deep generative model 2024.02; 15 (2): 180-192.e7. ( DOI )

  4. Subclonal accumulation of immune escape mechanisms in microsatellite instability-high colorectal cancers 2023.08; 129 (7): 1105-1118. ( PubMed, DOI )

  5. Topological data analysis of protein structure and inter/intra- molecular interaction changes attributable to amino acid mutations 2023.05; 21 2950-2959. ( DOI )

  6. 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 )

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

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

  9. Immunogenomic pan-cancer landscape reveals immune escape mechanisms and immunoediting histories 2021.08; 11 (1): 15713. ( PubMed, DOI )

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

  11. Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples 2020.09; 11 (1): 4748. ( DOI )

  12. Sex differences in oncogenic mutational processes 2020.08; 11 (1): 4330. ( DOI )

  13. Divergent mutational processes distinguish hypoxic and normoxic tumours 2020.02; 11 (1): 7569. ( DOI )

  14. Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer 2020.02; 52 (3): 294-305. ( PubMed, DOI )

  15. Comprehensive molecular characterization of mitochondrial genomes in human cancers 2020.02; 52 (3): 342-352. ( DOI )

  16. Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing 2020.02; 52 (3): 331-341. ( PubMed, DOI )

  17. Combined burden and functional impact tests for cancer driver discovery using DriverPower 2020.02; 11 (1): 7571. ( PubMed, DOI )

  18. Classification of primary liver cancer with immunosuppression mechanisms and correlation with genomic alterations 2020.02; 53 102659. ( PubMed, DOI )

  19. Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis 2020.02; 3 (1): 1347. ( PubMed, DOI )

  20. Butler enables rapid cloud-based analysis of thousands of human genomes 2020.02; 38 (3): 288-292. ( DOI )

  21. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes 2020.02; 578 (7793): 102-111. ( PubMed, DOI )

  22. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns 2020.02; 11 (1): 7573. ( PubMed, DOI )

  23. Genomic basis for RNA alterations in cancer 2020.02; 578 (7793): 129-136. ( DOI )

  24. The repertoire of mutational signatures in human cancer 2020.02; 578 (7793): 94-101. ( PubMed, DOI )

  25. The landscape of viral associations in human cancers 2020.02; 52 (3): 320-330. ( PubMed, DOI )

  26. The evolutionary history of 2,658 cancers 2020.02; 578 (7793): 122-128. ( DOI )

  27. Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig 2020.02; 11 (1): 7567. ( DOI )

  28. Patterns of somatic structural variation in human cancer genomes 2020.02; 578 (7793): 112-121. ( PubMed, DOI )

  29. Pathway and network analysis of more than 2500 whole cancer genomes 2020.02; 11 (1): 7566. ( PubMed, DOI )

  30. Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition 2020.02; 52 (3): 306-319. ( DOI )

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

  32. Integrative pathway enrichment analysis of multivariate omics data 2020.02; 11 (1): 7570. ( PubMed, DOI )

  33. Inferring structural variant cancer cell fraction 2020.02; 11 (1): 7568. ( DOI )

  34. High-coverage whole-genome analysis of 1220 cancers reveals hundreds of genes deregulated by rearrangement-mediated cis-regulatory alterations 2020.02; 11 (1): 7572. ( PubMed, DOI )

  35. Genomic footprints of activated telomere maintenance mechanisms in cancer 2020.02; 11 (1): 7574. ( PubMed, DOI )

  36. 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 )

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

  38. A Bayesian model integration for mutation calling through data partitioning 2019.03; 35 (21): 4247-4254. ( PubMed, DOI )

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

  40. A temporal shift of the evolutionary principle shaping intratumor heterogeneity in colorectal cancer 2018.07; 9 (1): 2884. ( PubMed, DOI )

  41. An in silico automated pipeline to identify tumor specic neoantigens from whole genome and exome sequencing data 2016;

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Books etc 【 display / non-display 】

  1. HLA Typing and Mutation Calling from Normal and Tumor Whole Genome Sequencing Data with ALPHLARD-NT. 2024.06

  2. Analyzing Antibody Repertoire Using Next-Generation Sequencing and Machine Learning.. 2022.11

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Conference Activities & Talks 【 display / non-display 】

  1. In silico DBTL cycle platform to design highly functional de novo proteins. 2024.09.18

  2. Acceleration of molecular dynamics simulation using deep learning. 2023.07.24

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

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

  5. A Bayesian Method for HLA genotyping from Whole Genome Sequencing Data. 2015.11.11

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

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