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2022 Fiscal Year Research-status Report

データコラボレーション解析による分散協調特徴量選択手法の研究

Research Project

Project/Area Number 22K12144
Research InstitutionUniversity of Tsukuba

Principal Investigator

叶 秀彩  筑波大学, システム情報系, 助教 (60814001)

Project Period (FY) 2022-04-01 – 2026-03-31
Keywordsfeature selection / active feature selection
Outline of Annual Research Achievements

In this year, we mainly address the active feature selection-based method and application. Most existing feature selection methods focus on statically selecting the same informative features for each subtype and fail to consider the heterogeneity of samples which causes pattern differences in each subtype. We consider active feature selection to dynamically acquire different features in each subtype by combining the subtype classifier with the reinforcement learning (RL) agent in a cost-sensitive manner. We apply active feature selection for gene signature identification in renal cell carcinoma, which can select different gene signatures for different renal cell carcinoma subtypes.

Current Status of Research Progress
Current Status of Research Progress

2: Research has progressed on the whole more than it was originally planned.

Reason

To explore different informative feature subsets for each subtype, we propose a novel active feature selection method and apply it to gene signature identification in renal cell carcinoma. By combining the subtype classifier with the reinforcement learning (RL) agent, our method can sequentially select the active features in each sample in a cost-sensitive manner. The application for gene signature identification in renal cell carcinoma show that our method can select different gene signatures for different renal cell carcinoma subtypes.

Strategy for Future Research Activity

Next step, we will further consider distributed datasets. We will propose distributed collaborative feature learning methods and consider privacy preserving. Intermediate representation based methods and federated learning will be applied to design the distributed feature selection framework for data collaboration. We also consider to apply our method to distributed multi-view datasets for multi-view data collaboration.

Causes of Carryover

Due to the COVID-19 pandemic, the international conference that I was planning to attend has been canceled.
I plan to join the international conference and collect the information about the latest research on feature learning and distributed data analysis.

  • Research Products

    (8 results)

All 2023 2022 Other

All Int'l Joint Research (1 results) Journal Article (3 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 3 results) Presentation (4 results) (of which Int'l Joint Research: 4 results,  Invited: 1 results)

  • [Int'l Joint Research] Xidian University(中国)

    • Country Name
      CHINA
    • Counterpart Institution
      Xidian University
  • [Journal Article] Interactive gene identification for cancer subtyping based on multi-omics clustering2023

    • Author(s)
      Ye Xiucai、Shi Tianyi、Cui Yaxuan、Sakurai Tetsuya
    • Journal Title

      Methods

      Volume: 211 Pages: 61~67

    • DOI

      10.1016/j.ymeth.2023.02.005

    • Peer Reviewed
  • [Journal Article] Sequential reinforcement active feature learning for gene signature identification in renal cell carcinoma2022

    • Author(s)
      Huang Meng、Ye Xiucai、Imakura Akira、Sakurai Tetsuya
    • Journal Title

      Journal of Biomedical Informatics

      Volume: 128 Pages: 104049~104049

    • DOI

      10.1016/j.jbi.2022.104049

    • Peer Reviewed
  • [Journal Article] Multiview network embedding for drug-target Interactions prediction by consistent and complementary information preserving2022

    • Author(s)
      Shang Yifan、Ye Xiucai、Futamura Yasunori、Yu Liang、Sakurai Tetsuya
    • Journal Title

      Briefings in Bioinformatics

      Volume: 23 Pages: -

    • DOI

      10.1093/bib/bbac059

    • Peer Reviewed / Int'l Joint Research
  • [Presentation] Collaborative Future Selection for Distributed Data2023

    • Author(s)
      Ye Xiucai; Imakura Akira; Sakurai Tetsuya
    • Organizer
      SIAM Conference on Computational Science and Engineering (CSE23)
    • Int'l Joint Research
  • [Presentation] Cost-Efficient Integrated Analysis of Distributed Data in Secure Environments2023

    • Author(s)
      Sakurai Tetsuya; Imakura Akira; Ye Xiucai; Bogdanova Anna
    • Organizer
      SIAM Conference on Computational Science and Engineering (CSE23)
    • Int'l Joint Research
  • [Presentation] Multiview Network Embedding for Drug-target Interaction Prediction2023

    • Author(s)
      Ye Xiucai; Shang Yifan; Futamura Yasunori; Sakurai Tetsuya
    • Organizer
      International Conference on Machine Learning and Computing (ICMLC)
    • Int'l Joint Research / Invited
  • [Presentation] Ensemble Learning for Cluster Number Detection Based on Shared Nearest Neighbor Graph and Spectral Clustering2022

    • Author(s)
      Weihang Zhang, Xiucai Ye, Testuya Sakurai
    • Organizer
      International Joint Conference on Neural Networks (IJCNN)
    • Int'l Joint Research

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Published: 2023-12-25  

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