Can neural network constraints in GP provide power to detect genes associated with human disease?

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "William S. Bush and Alison A. Motsinger and 
                 Scott M. Dudek and Marylyn D. Ritchie",
  title =        "Can neural network constraints in GP provide power to
                 detect genes associated with human disease?",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2005: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
                 {EvoIASP}, {EvoMUSART}, {EvoSTOC}",
  year =         "2005",
  month =        "30 " # mar # "-1 " # apr,
  editor =       "Franz Rothlauf and Juergen Branke and 
                 Stefano Cagnoni and David W. Corne and Rolf Drechsler and 
                 Yaochu Jin and Penousal Machado and Elena Marchiori and 
                 Juan Romero and George D. Smith and Giovanni Squillero",
  series =       "LNCS",
  volume =       "3449",
  publisher =    "Springer Verlag",
  address =      "Lausanne, Switzerland",
  publisher_address = "Berlin",
  pages =        "44--53",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computation, ANN",
  ISBN =         "3-540-25396-3",
  ISSN =         "0302-9743",
  DOI =          "doi:10.1007/b106856",
  abstract =     "A major goal of human genetics is the identification
                 of susceptibility genes associated with common, complex
                 diseases. Identifying gene-gene and gene-environment
                 interactions which comprise the genetic architecture
                 for a majority of common diseases is a difficult
                 challenge. To this end, novel computational approaches
                 have been applied to studies of human disease.
                 Previously, a GP neural network (GPNN) approach was
                 employed. Although the GPNN method has been quite
                 successful, a clear comparison of GPNN and GP alone to
                 detect genetic effects has not been made. In this
                 paper, we demonstrate that using NN evolved by GP can
                 be more powerful than GP alone. This is most likely due
                 to the confined search space of the GPNN approach, in
                 comparison to a free form GP. This study demonstrates
                 the benefits of using GP to evolve NN in studies of the
                 genetics of common, complex human disease.",
  notes =        "EvoWorkshops2005",

Genetic Programming entries for William S Bush Alison A Motsinger Scott M Dudek Marylyn D Ritchie