Cell-based metrics improve the detection of gene-gene interactions using multifactor dimensionality reduction

Abstract

Multifactor Dimensionality Reduction (MDR) is a widely-used data-mining method for detecting and interpreting epistatic effects that do not display significant main effects. MDR produces a reduced-dimensionality representation of a dataset which classifies multi-locus genotypes into either high- or low-risk groups. The weighted fraction of cases and controls correctly labelled by this classification, the balanced accuracy, is typically used as a metric to select the best or most-fit model. We propose two new metrics for MDR to use in evaluating models, Variance and Fisher, and compare those metrics to two previously-used MDR metrics, Balanced Accuracy and Normalized Mutual Information. We find that the proposed metrics consistently outperform the existing metrics across a variety of scenarios. © 2013 Springer-Verlag.