Document Type
Article
Publication Date
1-25-2006
Publication Title
BMC Bioinformatics
Department
Geisel School of Medicine
Abstract
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.
DOI
10.1186/1471-2105-7-39
Original Citation
Motsinger AA, Lee SL, Mellick G, Ritchie MD. GPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease. BMC Bioinformatics. 2006 Jan 25;7:39. doi: 10.1186/1471-2105-7-39. PMID: 16436204; PMCID: PMC1388239.
Dartmouth Digital Commons Citation
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). Dartmouth Scholarship. 571.
https://digitalcommons.dartmouth.edu/facoa/571
