The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease.
Motsinger, Alison A.; Lee, Stephen L.; Mellick, George; and Ritchie, Marylyn D., "GPNN: Power Studies and Applications of a Neural Network Method for Detecting Gene-Gene Interactions in Studies of Human Disease" (2006). Open Dartmouth: Faculty Open Access Articles. 571.