研究課題/領域番号 |
22K12150
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研究種目 |
基盤研究(C)
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配分区分 | 基金 |
応募区分 | 一般 |
審査区分 |
小区分61030:知能情報学関連
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研究機関 | 京都大学 |
研究代表者 |
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研究期間 (年度) |
2022-04-01 – 2025-03-31
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研究課題ステータス |
交付 (2022年度)
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配分額 *注記 |
4,160千円 (直接経費: 3,200千円、間接経費: 960千円)
2024年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2023年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2022年度: 1,560千円 (直接経費: 1,200千円、間接経費: 360千円)
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キーワード | Graph neural networks / machine learning / Machine learning / Structured data / Deep learning / Sparse learning |
研究開始時の研究の概要 |
There are three directions of this research project: (1) investigating original machine learning models for complicated structures, (2) designing novel structure discovery tools incorporating domain knowledge, and (3) discovering new biomedical knowledge to be used by domain experts.
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研究実績の概要 |
We are working on the topic of predicting properties of two drugs, formulated as a pair of graphs. Due to its potential high dimensionality and small scale data, we have to leverage more data from different sources to avoid overfitting. The solution is to learn representation of two drugs within a network of drugs, proteins and other biological information.
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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
We have found some small but novel methods for particular problem of learning representations of two drugs in its special context.
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今後の研究の推進方策 |
We continue to investigate learning problem with small data by leveraging information from other source to learn reliably.
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