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

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  • Ruczinski, Ingo
  • Kooperberg, Charles
  • L. LeBlanc, Michael

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.

Suggested Citation

  • Ruczinski, Ingo & Kooperberg, Charles & L. LeBlanc, Michael, 2004. "Exploring interactions in high-dimensional genomic data: an overview of Logic Regression, with applications," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 178-195, July.
    • Handle: RePEc:eee:jmvana:v:90:y:2004:i:1:p:178-195
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    References listed on IDEAS

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    1. Aarts, Emile H. L. & Korst, Jan H. M., 1989. "Boltzmann machines for travelling salesman problems," European Journal of Operational Research, Elsevier, vol. 39(1), pages 79-95, March.
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    Cited by:

    1. Malina Magdalena & Posch Martin & Ickstadt Katja & Schwender Holger & Bogdan Małgorzata, 2014. "Detection of epistatic effects with logic regression and a classical linear regression model," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(1), pages 83-104, February.
    2. Schwender, Holger & Ickstadt, Katja, 2006. "Identification of SNP interactions using logic regression," Technical Reports 2006,31, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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