Genetic programming neural networks: A powerful bioinformatics tool for human genetics

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Marylyn D. Ritchie and Alison A. Motsinger and 
                 William S. Bush and Christopher S. Coffey and Jason H. Moore",
  title =        "Genetic programming neural networks: A powerful
                 bioinformatics tool for human genetics",
  journal =      "Applied Soft Computing",
  year =         "2007",
  volume =       "7",
  number =       "1",
  pages =        "471--479",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, ANN, Neural
                 networks, Bioinformatics, Epistasis, Gene-gene
  DOI =          "doi:10.1016/j.asoc.2006.01.013",
  size =         "9 pages",
  abstract =     "The identification of genes that influence the risk of
                 common, complex disease 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 optimising the architecture of a neural
                 network to improve the identification of genetic and
                 gene environment combinations associated with disease
                 risk. Previous empirical studies suggest GPNN has
                 excellent power for identifying gene-gene and
                 gene-environment interactions. The goal of this study
                 was to compare the power of GPNN to stepwise logistic
                 regression (SLR) and classification and regression
                 trees (CART) for identifying gene-gene and gene
                 environment interactions. SLR and CART are standard
                 methods of analysis for genetic association studies.
                 Using simulated data, we show that GPNN has higher
                 power to identify gene-gene and gene-environment
                 interactions than SLR and CART. These results indicate
                 that GPNN may be a useful pattern recognition approach
                 for detecting gene gene and gene environment
                 interactions in studies of human disease.",

Genetic Programming entries for Marylyn D Ritchie Alison A Motsinger William S Bush Christopher S Coffey Jason H Moore