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• W2019465105 abstract "Interactions among genetic variants are likely to affect risk for human complex diseases, and their identification should increase the power to detect disease-associated variants and elucidate biological pathways underlying diseases. We propose a two-stage approach: model selection with Support Vector Machines identifies the most promising Single Nucleotide Polymorphisms and interactions logistic regression ensures a valid type I error by excluding non-significant candidates after Bonferroni correction. Simulation studies for case-control data suggest that our method powerfully detects gene-gene interactions. We analyze a published genome-wide casecontrol dataset, where our method successfully identifies an interaction term, which was missed in previous studies." @default.
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• W2019465105 date "2012-01-01" @default.
• W2019465105 modified "2023-10-16" @default.
• W2019465105 title "Support Vector Machines with L<SUB align=right>1 penalty for detecting gene-gene interactions" @default.
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• W2019465105 doi "https://doi.org/10.1504/ijdmb.2012.049300" @default.
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