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PMID: 16722772 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Machine learning for detecting gene-gene interactions: a review.

Applied bioinformatics ·Vol. 5 ·No. 2 ·2006-00-00 ·Pages 77-88

McKinney BA, Reif DM, Ritchie MD, Moore JH

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.

MeSH Terms
Algorithms Animals Artificial Intelligence Genome, Human Genomics/methods Genotype Humans Models, Genetic Models, Statistical Neural Networks, Computer Polymorphism, Single Nucleotide
Authors & Affiliations
4 authors, click to expand affiliations / ORCID
McKinney Brett A
Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University Medical School, Nashville, Tennessee, USA.
Reif David M
Ritchie Marylyn D
Moore Jason H
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Article Info
Journal
Applied bioinformatics
Abbr.
Appl Bioinformatics
ISSN
1175-5636
DOI
10.2165/00822942-200605020-00002
Published
2006-00-00
Pages
77-88
Language
English
Region
New Zealand
NLM ID
101150311
PMCID
PMC3244050
Subset
IM
Grants
NLM NIH HHS · LM009012 · United States
NHLBI NIH HHS · R01 HL065234 · United States
NHLBI NIH HHS · HL65234 · United States
NIEHS NIH HHS · P42 ES007373 · United States
NCRR NIH HHS · P20 RR018787 · United States
NIAID NIH HHS · K25 AI064625-01A1 · United States
NIEHS NIH HHS · ES007373 · United States
NCRR NIH HHS · RR018787 · United States
NICHD NIH HHS · HD047447 · United States
NIAID NIH HHS · AI064625 · United States
NIAID NIH HHS · R01 AI059694 · United States
NLM NIH HHS · R01 LM009012 · United States
NIAID NIH HHS · R01 AI057661 · United States
NIAID NIH HHS · K25 AI064625 · United States
NIAID NIH HHS · AI059694 · United States
NIAID NIH HHS · AI057661 · United States
NICHD NIH HHS · R01 HD047447 · United States
External Links
PubMed source DOI: 10.2165/00822942-200605020-00002 PMC Full Text
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