Performance of random forests and logic regression methods using mini-exome sequence data.
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ABSTRACT:
SUBMITTER: Kim Y
PROVIDER: S-EPMC3287827 | biostudies-other | 2011 Nov
REPOSITORIES: biostudies-other
Publications
Performance of random forests and logic regression methods using mini-exome sequence data.
Kim Yoonhee Y Li Qing Q Cropp Cheryl D CD Sung Heejong H Cai Juanliang J Simpson Claire L CL Perry Brian B Dasgupta Abhijit A Malley James D JD Wilson Alexander F AF Bailey-Wilson Joan E JE
BMC proceedings 20111129
Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within gene ...[more]
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