Machine Learning for Detecting Gene-Gene Interactions

  title={Machine Learning for Detecting Gene-Gene Interactions},
  author={Brett A. McKinney and David M. Reif and Marylyn DeRiggi Ritchie and Jason H. Moore},
  journal={Applied Bioinformatics},
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… 

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Computational analysis of gene-gene interactions using multifactor dimensionality reduction

  • J. Moore
  • Biology
    Expert review of molecular diagnostics
  • 2004
A novel strategy known as multifactor dimensionality reduction that was specifically designed for the identification of multilocus genetic effects is presented and several case studies that demonstrate the detection of gene–gene interactions in common diseases such as atrial fibrillation, Type II diabetes and essential hypertension are discussed.

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