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

Ingo Ruczinski, Charles Kooperberg, Michael L. LeBlanc

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)178-195
Number of pages18
JournalJournal of Multivariate Analysis
Volume90
Issue number1 SPEC. ISS.
DOIs
StatePublished - Jul 2004

Keywords

  • Adaptive model selection
  • Binary variables
  • Boolean logic
  • Interactions
  • Single nucleotide polymorphisms

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

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