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.
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Fisher, J.M., Andrews, P., Kiralis, J., Sinnott-Armstrong, N.A., Moore, J.H. (2013). Cell-Based Metrics Improve the Detection of Gene-Gene Interactions Using Multifactor Dimensionality Reduction. In: Vanneschi, L., Bush, W.S., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2013. Lecture Notes in Computer Science, vol 7833. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37189-9_18
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DOI: https://doi.org/10.1007/978-3-642-37189-9_18
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