Grammatical Evolution of Neural Networks for Discovering Epistasis among Quantitative Trait Loci

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  title =        "Grammatical Evolution of Neural Networks for
                 Discovering Epistasis among Quantitative Trait Loci",
  author =       "Stephen D. Turner and Scott M. Dudek and 
                 Marylyn D. Ritchie",
  booktitle =    "8th European Conference on Evolutionary Computation,
                 Machine Learning and Data Mining in Bioinformatics
                 (EvoBIO 2010)",
  publisher =    "Springer",
  year =         "2010",
  editor =       "Clara Pizzuti and Marylyn D. Ritchie and 
                 Mario Giacobini",
  volume =       "6023",
  pages =        "86--97",
  series =       "Lecture Notes in Computer Science",
  address =      "Istanbul, Turkey",
  month =        apr # " 7-9",
  keywords =     "genetic algorithms, genetic programming, grammatical
  isbn13 =       "978-3-642-12210-1",
  DOI =          "doi:10.1007/978-3-642-12211-8",
  abstract =     "A fundamental goal of human genetics is the discovery
                 of polymorphisms that predict common, complex diseases.
                 It is hypothesized that complex diseases are due to a
                 myriad of factors including environmental exposures and
                 complex genetic risk models, including gene-gene
                 interactions. Such interactive models present an
                 important analytical challenge, requiring that methods
                 perform both variable selection and statistical
                 modeling to generate testable genetic model hypotheses.
                 Decision trees are a highly successful, easily
                 interpretable data-mining method that are typically
                 optimized with a hierarchical model building approach,
                 which limits their potential to identify interactive
                 effects. To overcome this limitation, we use
                 evolutionary computation, specifically grammatical
                 evolution, to build decision trees to detect and model
                 gene-gene interactions. Currently, we introduce the
                 Grammatical Evolution Decision Trees (GEDT) method, and
                 demonstrate that GEDT has power to detect interactive
                 models in a range of simulated data, revealing GEDT to
                 be a promising new approach for human genetics.",
  bibdate =      "2010-04-13",
  bibsource =    "DBLP,
  affiliation =  "North Carolina State University Department of Computer
                 Science Raleigh NC USA 27695",

Genetic Programming entries for Stephen D Turner Scott M Dudek Marylyn D Ritchie