Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases

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@Article{Ritchie:2003:BMCB,
  author =       "Marylyn D. Ritchie and Bill C. White and 
                 Joel S. Parker and Lance W. Hahn and Jason H. Moore",
  title =        "Optimization of neural network architecture using
                 genetic programming improves detection and modeling of
                 gene-gene interactions in studies of human diseases",
  journal =      "BMC Bioinformatics",
  year =         "2003",
  volume =       "4",
  number =       "28",
  month =        "7 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.biomedcentral.com/1471-2105/4/28",
  DOI =          "doi:10.1186/1471-2105-4-28",
  size =         "14 pages",
  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. Conclusion
                 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.",
  notes =        "Cited by New application of intelligent agents in
                 sporadic amyotrophic lateral sclerosis identifies
                 unexpected specific genetic background Silvana Penco,
                 Massimo Buscema, Maria Cristina Patrosso, Alessandro
                 Marocchi, and Enzo Grossi BMC Bioinformatics. 2008; 9:
                 254. Published online 2008 May 30.
                 doi:10.1186/1471-2105-9-254. PMCID: PMC2443147",
}

Genetic Programming entries for Marylyn D Ritchie Bill C White Joel S Parker Lance W Hahn Jason H Moore

Citations