A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility.

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Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions (2009, background): “briefly discuss both filter and wrapper options as part of an overall epistasis analysis strategy for human disease susceptibility [16] and Greene et al.”
A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction (2007, methodology): “…mining strategy for identifying combinations of single-nucleotide polymorphisms (SNPs) and other discrete factors such as smoking that are predictive of a discrete clinical endpoint [Ritchie et al., 2001, 2003; Hahn et al., 2003; Hahn and Moore, 2004; Moore, 2004; Moore et al., 2006; Moore, 2007].”
Naïve Bayesian Classifier and Genetic Risk Score for Genetic Risk Prediction of a Categorical Trait: Not so Different after all! (2012, background): “, 2010), multifactor dimensionality reduction (Moore et al., 2006), and Bayesian networks (Rodin and Boerwinkle, 2005; Sebastiani et al.”
Discovering causal interactions using Bayesian network scoring and information gain (2016, methodology): “…substantially outperformed nine epistasis learning methods including MBS [29], LEAP [31], logistic regression [18], MDR [33] combined with a heuristic search, full interaction modeling [21], information gain alone [22], SNP Harvester [23], BEAM [27], and a technique that uses maximum entropy [28].”