Support vector machines with L1 penalty for detecting gene-gene interactions.

Abstract excerpt

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...

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