2015 Fiscal Year Final Research Report
Development of machine learning methods to comprehensively predict drug targets from omics data
| Project/Area Number |
25700029
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| Research Category |
Grant-in-Aid for Young Scientists (A)
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| Allocation Type | Partial Multi-year Fund |
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| Research Field |
Life / Health / Medical informatics
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| Research Institution | Kyushu University |
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Principal Investigator |
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| Project Period (FY) |
2013-04-01 – 2016-03-31
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| Keywords | 機械学習 / インシリコ創薬 / 標的分子 / 相互作用予測 / 薬物 |
|---|
| Outline of Final Research Achievements |
The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Various high-throughput experimental projects enable us to analyze the genome, transcriptome, proteome. At the same time, the high-throughput screening of large-scale chemical compound libraries with various biological assays produce chemical and phenotypic data on drugs and bioactive compounds. In this project, we develop machine learning methods to infer unknown drug-target interactions by integrating various large-scale omics data.
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| Free Research Field |
バイオインフォマティクス
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