Grammatical evolution decision trees for detecting gene-gene interactions

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@Article{Motsinger-Reif:2010:BDM,
  author =       "Alison Motsinger-Reif and Sushamna Deodhar and 
                 Stacey Winham and Nicholas Hardison",
  title =        "Grammatical evolution decision trees for detecting
                 gene-gene interactions",
  journal =      "BioData Mining",
  volume =       "3",
  year =         "2010",
  number =       "1",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution Decision Trees",
  URL =          "http://www.biodatamining.org/content/3/1/8",
  DOI =          "doi:10.1186/1756-0381-3-8",
  pubmedid =     "21087514",
  ISSN =         "1756-0381",
  size =         "15 pages",
  abstract =     "BACKGROUND:

                 A fundamental goal of human genetics is the discovery
                 of polymorphisms that predict common, complex diseases.
                 It is suggested that complex diseases are due to a
                 myriad of factors including environmental exposures and
                 complex genetic risk models, including gene-gene
                 interactions. Such epistatic models present an
                 important analytical challenge, requiring that methods
                 perform not only statistical modelling, but also
                 variable selection to generate testable genetic model
                 hypotheses. This challenge is amplified by recent
                 advances in genotyping technology, as the number of
                 potential predictor variables is rapidly
                 increasing.

                 METHODS:

                 Decision trees are a highly successful, easily
                 interpretable data-mining method that are typically
                 optimised with a hierarchical model building approach,
                 which limits their potential to identify interacting
                 effects. To overcome this limitation, we use
                 evolutionary computation, specifically grammatical
                 evolution, to build decision trees to detect and model
                 gene-gene interactions. In the current study, we
                 introduce the Grammatical Evolution Decision Trees
                 (GEDT) method and software and evaluate this approach
                 on simulated data representing gene-gene interaction
                 models of a range of effect sizes. We compare the
                 performance of the method to a traditional decision
                 tree algorithm and a random search approach and
                 demonstrate the improved performance of the method to
                 detect purely epistatic interactions.

                 RESULTS:

                 The results of our simulations demonstrate that GEDT
                 has high power to detect even very moderate genetic
                 risk models. GEDT has high power to detect interactions
                 with and without main effects.

                 CONCLUSIONS:

                 GEDT, while still in its initial stages of development,
                 is a promising new approach for identifying gene-gene
                 interactions in genetic association studies.",
}

Genetic Programming entries for Alison A Motsinger Sushamna Deodhar Stacey J Winham Nicholas E Hardison

Citations