The power of quantitative grammatical evolution neural networks to detect gene-gene interactions

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Nicholas E. Hardison and Alison A. Motsinger-Reif",
  title =        "The power of quantitative grammatical evolution neural
                 networks to detect gene-gene interactions",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0557-0",
  pages =        "299--306",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, Bioinformatics, computational, systems, and
                 synthetic biology",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001618",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Applying grammatical evolution to evolve neural
                 networks (GENN) has been increasing used in genetic
                 epidemiology to detect gene-gene or gene-environment
                 interactions, also known as epistasis, in high
                 dimensional data. GENN approaches have previously been
                 shown to be highly successful in a range of simulated
                 and real case-control studies, and has recently been
                 applied to quantitative traits. In the current study,
                 we evaluate the potential of an application of GENN to
                 quantitative traits (QTGENN) to a range of simulated
                 genetic models. We demonstrate the power of the
                 approach, and compare this power to more traditional
                 linear regression analysis approaches. We find that the
                 QTGENN approach has relatively high power to detect
                 both single-locus models as well as several completely
                 epistatic two-locus models, and favourably compares to
                 the regression methods.",
  notes =        "Also known as \cite{2001618} GECCO-2011 A joint
                 meeting of the twentieth international conference on
                 genetic algorithms (ICGA-2011) and the sixteenth annual
                 genetic programming conference (GP-2011)",

Genetic Programming entries for Nicholas E Hardison Alison A Motsinger