Feature selection with interactions in logistic regression models using multivariate synergies for a GWAS application.

ABSTRACT:

Background

Genotype-phenotype association has been one of the long-standing problems in bioinformatics. Identifying both the marginal and epistatic effects among genetic markers, such as Single Nucleotide Polymorphisms (SNPs), has been extensively integrated in Genome-Wide Association Studies (GWAS) to help derive "causal" genetic risk factors and their interactions, which play critical roles in life and disease systems. Identifying "synergistic" interactions with respect to the outcome of interest can help accurate phenotypic prediction and understand the underlying mechanism of system behavior. Many statistical measures for estimating synergistic interactions have been proposed in the literature for such a purpose. However, except for empirical performance, there is still no theo

SUBMITTER: Xu EL 

PROVIDER: S-EPMC5872388 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature