• Search Research Projects
  • Search Researchers
  • How to Use
  1. Back to previous page

Takahashi Tomoei  高橋 智栄

ORCIDConnect your ORCID iD *help
Researcher Number 10980691
Other IDs
Affiliation (Current) 2026: 静岡大学, 工学部, 助教
Affiliation (based on the past Project Information) *help 2023 – 2025: 東京大学, 大学院理学系研究科(理学部), 特任研究員
Review Section/Research Field
Principal Investigator
Basic Section 61040:Soft computing-related / 1002:Human informatics, applied informatics and related fields
Keywords
Principal Investigator
情報統計力学 / 確率伝搬法 / タンパク質デザイン / グラフニューラルネットワーク / ダーウィン進化 / 頑健性 / 周辺尤度最大化
  • Research Projects

    (2 results)
  • Research Products

    (6 results)
  •  Protein design through the integration of statistical mechanics and artificial intelligencePrincipal Investigator

    • Principal Investigator
      高橋 智栄
    • Project Period (FY)
      2025 – 2029
    • Research Category
      Grant-in-Aid for Early-Career Scientists
    • Review Section
      Basic Section 61040:Soft computing-related
    • Research Institution
      The University of Tokyo
  •  Statistical Mechanical Informatics Approach to Protein Design ProblemPrincipal Investigator

    • Principal Investigator
      高橋 智栄
    • Project Period (FY)
      2023 – 2025
    • Research Category
      Grant-in-Aid for Research Activity Start-up
    • Review Section
      1002:Human informatics, applied informatics and related fields
    • Research Institution
      The University of Tokyo

All 2025 2024 2023

All Journal Article Presentation

  • [Journal Article] Alpha helices are more evolutionarily robust to environmental perturbations than beta sheets: Bayesian learning and statistical mechanics for protein evolution2025

    • Author(s)
      Tomoei Takahashi, George Chikenji, Kei Tokita, and Yoshiyuki Kabashima
    • Journal Title

      Physical Review Research

      Volume: 7 Issue: 2 Pages: 1-16

    • DOI

      10.1103/physrevresearch.7.023115

    • Peer Reviewed / Open Access
    • Data Source
      KAKENHI-PROJECT-23K19996
  • [Presentation] Alpha helices are more evolutionarily robust to environmental perturbations than beta sheets: Bayesian theory for evolution2025

    • Author(s)
      Tomoei Takahashi, George Chikenji, Kei Tokita, and Yoshiyuki Kabashima
    • Organizer
      American Physical Society Global Physics Summit 2025
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-23K19996
  • [Presentation] 格子タンパク質模型の二次構造と 環境変化に対する進化的な頑健性との相関2024

    • Author(s)
      高橋智栄, 千見寺浄慈, 時田恵一郎, 樺島祥介
    • Organizer
      日本物理学会78回年次大会, 北海道大学
    • Data Source
      KAKENHI-PROJECT-23K19996
  • [Presentation] 経験ベイズ法を用いたタンパク質のアミノ酸残基間相関関数の解析2024

    • Author(s)
      高橋智栄
    • Organizer
      2024年日本物理学会春季大会
    • Data Source
      KAKENHI-PROJECT-23K19996
  • [Presentation] 経験ベイズ法を用いた タンパク質のアミノ酸残基間相関関数の解析2024

    • Author(s)
      高橋智栄, 千見寺浄慈, 時田恵一郎, 樺島祥介
    • Organizer
      2024年日本物理学会春季大会
    • Data Source
      KAKENHI-PROJECT-23K19996
  • [Presentation] An Empirical Bayes Approach to Estimate the Chemical Potential of Water in Protein Design Problem2023

    • Author(s)
      高橋智栄
    • Organizer
      International conference on MACHINE LEARNING PHYSICS
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-23K19996

URL: 

Are you sure that you want to link your ORCID iD to your KAKEN Researcher profile?
* This action can be performed only by the researcher himself/herself who is listed on the KAKEN Researcher’s page. Are you sure that this KAKEN Researcher’s page is your page?

この研究者とORCID iDの連携を行いますか?
※ この処理は、研究者本人だけが実行できます。

Information User Guide FAQ News Terms of Use Attribution of KAKENHI

Powered by NII kakenhi