Grammatical evolution decision trees for trio designs

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Amanda English and Holly Petruso and Chong Wang",
  title =        "Grammatical evolution decision trees for trio
  booktitle =    "Tenth GECCO Undergraduate Student Workshop",
  year =         "2012",
  editor =       "Sherri Goings",
  isbn13 =       "978-1-4503-1178-6",
  keywords =     "genetic algorithms, genetic programming, Grammatical
  pages =        "559--562",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330784.2330873",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The detection of gene-gene and gene-interactions in
                 genetic association studies is an important challenge
                 in human genetics. The detection of such interactive
                 models presents a difficult computational and
                 statistical challenge, especially as advances in
                 genotyping technology have rapidly expanded the number
                 of potential genetic predictors in such studies. The
                 scale of these studies makes exhaustive search
                 approaches infeasible, inspiring the application of
                 evolutionary computation algorithms to perform variable
                 selection and build classification models. Recently, an
                 application of grammatical evolution to evolve decision
                 trees (GEDT) has been introduced for detecting
                 interaction models. Initial results were promising, but
                 the previous applications of GEDT have been limited to
                 case-control studies with unrelated individuals. While
                 this study design is popular in human genetics, other
                 designs with related individuals offer distinct
                 advantages. Specifically, a trio-based design (with
                 genetic data for an affected individual and their
                 parents collected) can be a powerful approach to
                 mapping that is robust to population heterogeneity and
                 other potential confounders. In the current study, we
                 extend the GEDT approach to be able to handle trio data
                 (trioGEDT), and demonstrate its potential in simulated
                 data with gene-gene interactions that underlie disease
  notes =        "Also known as \cite{2330873} Distributed at

                 ACM Order Number 910122.",

Genetic Programming entries for Amanda English Holly Elizabeth Petruso Chong Wang