| Project/Area Number |
24790336
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Human genetics
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| Research Institution | Hamamatsu University School of Medicine |
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Principal Investigator |
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| Co-Investigator(Renkei-kenkyūsha) |
NISHIO Takuhiro 浜松医科大学, 医学部, 准教授 (90172626)
KANEKO Sunao 湊病院, 北東北てんかんセンター, センター長 (40106852)
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| Project Period (FY) |
2012-04-01 – 2014-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2013: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2012: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 遺伝子型-表現型 / 機械学習法 / SCN1A / 表現型予測 / 遺伝子診断 / 確率モデル / 遺伝型-表現型相関 |
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| Research Abstract |
We described a computational model for phenotype prediction of SCN1A-related epilepsies involving the Machine-learning approaches (such as Support vector machines, Random Forest) that were trained some predicting factors obtained by data-mining analysis among the databases. Our models show high accuracy, especially, the prediction models include IE, P and HP of physicochemical property as predicting factor. Our findings indicate the possibility of phenotype prediction for entirely new missense mutations by an application of the physico-chemical properties of amino acid residues.
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