2023 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
| Project/Area Number |
22K12144
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| Research Institution | University of Tsukuba |
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Principal Investigator |
叶 秀彩 筑波大学, システム情報系, 准教授 (60814001)
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| Project Period (FY) |
2022-04-01 – 2026-03-31
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| Keywords | feature learning |
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| Outline of Annual Research Achievements |
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.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
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.
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| Strategy for Future Research Activity |
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.
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| Causes of Carryover |
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.
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