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2023 年度 実施状況報告書

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

研究課題

研究課題/領域番号 22K12144
研究機関筑波大学

研究代表者

叶 秀彩  筑波大学, システム情報系, 准教授 (60814001)

研究期間 (年度) 2022-04-01 – 2026-03-31
キーワードfeature learning
研究実績の概要

This year, we focus on the research on feature learning-based methods and their application in bioinformatics. To address the challenges of limited samples and data imbalance, we present a novel framework for feature learning to analyze complex data structures, which are applied to predict antibiotic activity and enhance the efficiency of antibiotic discovery. In order to effectively learn the complex data, we utilize contrastive learning to extract the important features from complex structures. By integrating data augmentation and a pre-trained RoBERTa model, our method is able to accurately predict the hEFG blocker.

現在までの達成度 (区分)
現在までの達成度 (区分)

2: おおむね順調に進展している

理由

In addressing the challenges posed by limited samples and imbalances within them, we introduce innovative methods to enhance feature learning and analyze complex data structures. Our approaches employ contrastive learning to effectively capture critical features from complex datasets, and integrates data augmentation alongside a pre-trained RoBERTa model to predict hEFG blockers with high precision, thus advancing the field of antibiotic discovery.

今後の研究の推進方策

In the next step, we will address with data that is distributed in different locations. We're going to propose methods that learn from the data together without compromising privacy. We'll use effective techniques, including federated learning, to learn important features from the data. We're also planning to apply these methods to data that comes from multiple perspectives to improve how we combine and use this kind of data.

次年度使用額が生じた理由

This year, due to the high number of classes scheduled for the fall semester, I am unable to participate in some international conferences.
Nest year, I plan to join the international conference and collect the information about the latest research on feature learning and distributed data analysis.

  • 研究成果

    (7件)

すべて 2024 2023

すべて 雑誌論文 (3件) (うち査読あり 3件) 学会発表 (3件) (うち国際学会 3件) 産業財産権 (1件)

  • [雑誌論文] FIAMol-AB: A feature fusion and attention-based deep learning method for enhanced antibiotic discovery2024

    • 著者名/発表者名
      He Shida、Ye Xiucai、Dou Lijun、Sakurai Tetsuya
    • 雑誌名

      Computers in Biology and Medicine

      巻: 168 ページ: 107762~107762

    • DOI

      10.1016/j.compbiomed.2023.107762

    • 査読あり
  • [雑誌論文] CLOP-hERG: The Contrastive Learning Optimized Pre-Trained Model for Representation Learning in Predicting Drug-Induced hERG Channel Blockers2024

    • 著者名/発表者名
      He Shida、Ye Xiucai、Sakurai Tetsuya
    • 雑誌名

      Medinformatics

      巻: - ページ: 1~9

    • DOI

      10.47852/bonviewMEDIN42022049

    • 査読あり
  • [雑誌論文] Multi-omics clustering for cancer subtyping based on latent subspace learning2023

    • 著者名/発表者名
      Ye Xiucai、Shang Yifan、Shi Tianyi、Zhang Weihang、Sakurai Tetsuya
    • 雑誌名

      Computers in Biology and Medicine

      巻: 164 ページ: 107223~107223

    • DOI

      10.1016/j.compbiomed.2023.107223

    • 査読あり
  • [学会発表] Multi-omics clustering based on interpretable and discriminative features for cancer subtyping2023

    • 著者名/発表者名
      Tianyi Shi, Xiucai Ye, Tetsuya Sakurai
    • 学会等名
      IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
    • 国際学会
  • [学会発表] Multi-view Network Embedding with Structure and Semantic Contrastive Learning2023

    • 著者名/発表者名
      Yifan Shang, Xiucai Ye, Tetsuya Sakurai
    • 学会等名
      IEEE International Conference on Multimedia and Expo (ICME)
    • 国際学会
  • [学会発表] Common and Unique Features Learning in Multi-view Network Embedding2023

    • 著者名/発表者名
      Yifan Shang, Xiucai Ye, Tetsuya Sakurai
    • 学会等名
      International Joint Conference on Neural Networks (IJCNN)
    • 国際学会
  • [産業財産権] 特徴量選択支援プログラム及び特徴量選択支援方法2023

    • 発明者名
      今倉暁, 叶 秀彩, 櫻井鉄也
    • 権利者名
      今倉暁, 叶 秀彩, 櫻井鉄也
    • 産業財産権種類
      特許
    • 産業財産権番号
      7302851

URL: 

公開日: 2024-12-25  

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