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.

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