GPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease.

ABSTRACT:

Background

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.

Results

We show that GPNN has high power to detect even relatively small genetic eff

SUBMITTER: Motsinger AA 

PROVIDER: S-EPMC1388239 | biostudies-literature | 2006 Jan

REPOSITORIES: biostudies-literature