Exploring interactions in high-dimensional genomic data: an overview of Logic Regression, with applications

Ingo Ruczinski, Charles Kooperberg and Michael L. LeBlanc

Journal of Multivariate Analysis, 2004, vol. 90, issue 1, 178-195

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.

Keywords: Adaptive; model; selection; Boolean; logic; Binary; variables; Interactions; Single; nucleotide; polymorphisms (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (2)

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