Genetic programming neural networks as a bioinformatics tool for human genetics

Marylyn D. Ritchie, Christopher S. Coffey, Jason H. Moore

Research output: Contribution to journalArticle

30 Scopus citations

Abstract

The identification of genes that influence the risk of common, complex diseases primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming 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. Previous empirical studies suggest GPNN has excellent power for identifying gene-gene interactions. The goal of this study was to compare the power of GPNN and stepwise logistic regression (SLR) for identifying gene-gene interactions. Using simulated data, we show that GPNN has higher power to identify gene-gene interactions than SLR. These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene interactions.

Original languageEnglish (US)
Pages (from-to)438-448
Number of pages11
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3102
StatePublished - Dec 1 2004

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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