Development of genotype-phenotype prediction method for channelopathy
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 |
Research Field |
Human genetics
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Research Institution | Hamamatsu University School of Medicine |
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)
|
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 |
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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Report
(3 results)
Research Products
(22 results)
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[Journal Article] Anger in epilepsy patients with depression and patients with idiopathic major depression: A comparison study2014
Author(s)
Mori Y, Kanemoto K, Onuma T, Tanaka M, Oshima T, Kato H, Tachimori H, Wada K, Kikuchi T, Tomita T, Chen L, Liu F, Yoshida S, Kato M, Kaneko S
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Journal Title
The Tohoku Journal of Experimental Medicine
Volume: 232
Pages: 123-128
Related Report
Peer Reviewed
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