A comparison of internal validation techniques for multifactor dimensionality reduction.

BMC Bioinformatics
Stacey J WinhamAlison A Motsinger-Reif

Abstract

It is hypothesized that common, complex diseases may be due to complex interactions between genetic and environmental factors, which are difficult to detect in high-dimensional data using traditional statistical approaches. Multifactor Dimensionality Reduction (MDR) is the most commonly used data-mining method to detect epistatic interactions. In all data-mining methods, it is important to consider internal validation procedures to obtain prediction estimates to prevent model over-fitting and reduce potential false positive findings. Currently, MDR utilizes cross-validation for internal validation. In this study, we incorporate the use of a three-way split (3WS) of the data in combination with a post-hoc pruning procedure as an alternative to cross-validation for internal model validation to reduce computation time without impairing performance. We compare the power to detect true disease causing loci using MDR with both 5- and 10-fold cross-validation to MDR with 3WS for a range of single-locus and epistatic disease models. Additionally, we analyze a dataset in HIV immunogenetics to demonstrate the results of the two strategies on real data. MDR with 3WS is computationally approximately five times faster than 5-fold cross-vali...Continue Reading

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Methods Mentioned

BETA
genotyping

Software Mentioned

3WS
R
SASv9
genomeSIM
MDR
SimPen

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