Machine learning for detecting gene-gene interactions: A review

Brett A. McKinney, David M. Reif, Marylyn D. Ritchie, Jason H. Moore

Research output: Contribution to journalReview article

143 Scopus citations

Abstract

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are 'the norm' and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics.

Original languageEnglish (US)
Pages (from-to)77-88
Number of pages12
JournalApplied Bioinformatics
Volume5
Issue number2
DOIs
StatePublished - Jun 14 2006

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Agricultural and Biological Sciences(all)
  • Computer Science Applications

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