title: Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases.
abstract: BACKGROUND
  Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases.
  
  
  RESULTS
  Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present.
  
  
  CONCLUSIONS
  This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases.
authors.affiliation: Program in Human Genetics and Department of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, TN, 37232-0700, USA. ritchie@phg.mc.vanderbilt.edu
authors.forename: Marylyn D
authors.initials: MD
authors.lastname: Ritchie
authors.forename: Bill C
authors.initials: BC
authors.lastname: White
authors.forename: Joel S
authors.initials: JS
authors.lastname: Parker
authors.forename: Lance W
authors.initials: LW
authors.lastname: Hahn
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004843:Epistasis, Genetic; D005786:Gene Expression Regulation / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D017720:Molecular Epidemiology / Q000379:methods* / Q000639:trends*; D016571:Neural Networks, Computer; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D011237:Predictive Value of Tests; D012107:Research Design; D012984:Software; D012986:Software Validation