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GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease |
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| Authors: | Alison A Motsinger Stephen L Lee George Mellick and Marylyn D Ritchie |
| Institution: | (1) Center for Human Genetics Research and Department of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, TN 37232-0700, USA;(2) Dartmouth Medical School, One Medical Center Drive, Lebanon, New Hampshire 03756-001, USA;(3) Department of Neurology, Princess Alexandra Hospital, University of Queensland, School of Medicine, Brisbane, Australia |
| Abstract: | BackgroundThe 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. |
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