A comparison of internal validation techniques for multifactor dimensionality reduction

Background: 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 ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Stacey J. Winham, Andrew J. Slater, Alison A. Motsinger-Reif
• Keywords: BMCBI 2010, Disease, False Positive, Internal Validation