Exploring interactions in high-dimensional genomic data: an overview of logic regression, with applications

Authors:
Ingo Ruczinski;Charles Kooperbreg;Michael L. LeBlanc
Affiliations:
Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe St., Baltimore, MO;Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Fairview Ave. N, Seattle, WA;Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Fairview Ave. N, Seattle, WA
Venue:
Journal of Multivariate Analysis
Year:
2004

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Abstract

Logic Regression is an adaptive regression methodology mainly developed to explore high-order interactions in genomic data. Logic Regression is intended for situations where most of the covariates in the data to be analyzed are binary. The goal of Logic Regression is to find predictors that are Boolean (logical) combinations of the original predictors. In this article, we give an overview of the methodology and discuss some applications. We also describe the software for Logic Regression, which is available as an R and S-Plus package.