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

Ingo Ruczinski; Charles Kooperberg; Michael L. LeBlanc

doi:10.1016/j.jmva.2004.02.010

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

Ingo Ruczinski, Charles Kooperberg, Michael L. LeBlanc

Bloomberg School of Public Health

Research output: Contribution to journal › Article › peer-review

59 Scopus citations

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 language	English (US)
Pages (from-to)	178-195
Number of pages	18
Journal	Journal of Multivariate Analysis
Volume	90
Issue number	1 SPEC. ISS.
DOIs	https://doi.org/10.1016/j.jmva.2004.02.010
State	Published - 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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10.1016/j.jmva.2004.02.010

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

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, Binary variables, Boolean logic, Interactions, Single nucleotide polymorphisms",

author = "Ingo Ruczinski and Charles Kooperberg and LeBlanc, {Michael L.}",

note = "Funding Information: Ingo Ruczinski was supported in part by NIH Grant CA 83079 and through the support of the Maryland Cigarette Restitution Fund Research Grant to the Johns Hopkins Medical Institutions. Charles Kooperberg was supported in part by NIH Grants CA 74841 and CA 53996. Michael LeBlanc was supported in part by NIH Grants CA 90998 and CA 53996. The GAW workshop is supported by NIH Grant GM 31375. The authors thank Drs. Klaus Lindpainter, Jurg Ott, and Robert Zee for sharing the data on post-PTCA restenosis, and the organizers of the Genetic Analysis Workshop for their permission to use the GAW12 data in this paper.",

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AU - LeBlanc, Michael L.

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KW - Boolean logic

KW - Interactions

KW - Single nucleotide polymorphisms

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

Abstract

Keywords

ASJC Scopus subject areas

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