| Project/Area Number |
22700312
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Bioinformatics/Life informatics
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| Research Institution | Gunma University |
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Principal Investigator |
KATO Tsuyoshi 群馬大学, 大学院・工学研究科, 准教授 (40401236)
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| Project Period (FY) |
2010 – 2011
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| Project Status |
Completed (Fiscal Year 2011)
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| Budget Amount *help |
¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2011: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2010: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 非線形合成記述子 / サポートベクトルマシン / 転移学習 / 重みつき経験分布 / リガンド予測 / 一般化固有値問題 / P450アイソザイム / 嗅覚受容体 / リガンド / 機械学習 / 記述子 / 化合物 / アルゴリズム |
|---|
| Research Abstract |
This study tackled the problem for ligand prediction. The problem is to predict whether specified chemical compounds are interacted with specified target proteins. To achieve accurate prediction of ligands interacting with target proteins, effective descriptors that describe the properties of chemical compounds are crucial. Global models cannot deal with descriptors specific to each local prediction problem. Local models suffer from small sample size problems. This study introduced a concept of transfer learning to develop automatic descriptor extraction algorithms that solve the above two problems simultaneously.
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