GPDTI: A Genetic Programming Decision Tree Induction method to find epistatic effects in common complex diseases

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@Article{Estrada-Gil:2007:BI,
  author =       "Jesus K. Estrada-Gil and Juan C. Fernandez-Lopez and 
                 Enrique Hernandez-Lemus and Irma Silva-Zolezzi and 
                 Alfredo Hidalgo-Miranda and Gerardo Jimenez-Sanchez and 
                 Edgar E. Vallejo-Clemente",
  title =        "GPDTI: A Genetic Programming Decision Tree Induction
                 method to find epistatic effects in common complex
                 diseases",
  journal =      "Bioinformatics",
  year =         "2007",
  volume =       "13",
  number =       "13",
  pages =        "i167--i174",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1460-2059",
  DOI =          "doi:10.1093/bioinformatics/btm205",
  abstract =     "Motivation: The identification of risk-associated
                 genetic variants in common diseases remains a challenge
                 to the biomedical research community. It has been
                 suggested that common statistical approaches that
                 exclusively measure main effects are often unable to
                 detect interactions between some of these variants.
                 Detecting and interpreting interactions is a
                 challenging open problem from the statistical and
                 computational perspectives. Methods in computing
                 science may improve our understanding on the mechanisms
                 of genetic disease by detecting interactions even in
                 the presence of very low heritabilities.

                 Results: We have implemented a method using Genetic
                 Programming that is able to induce a Decision Tree to
                 detect interactions in genetic variants. This method
                 has a cross-validation strategy for estimating
                 classification and prediction errors and tests for
                 consistencies in the results. To have better estimates,
                 a new consistency measure that takes into account
                 interactions and can be used in a genetic programming
                 environment is proposed. This method detected five
                 different interaction models with heritabilities as low
                 as 0.008 and with prediction errors similar to the
                 generated errors.

                 Availability: Information on the generated data sets
                 and executable code is available upon request.",
  notes =        "PMID: 17646293 [PubMed - in process]",
}

Genetic Programming entries for Jesus Karol Estrada Gil Juan Carlos Fernandez Lopez Enrique Hernandez Lemus Irma Silva-Zolezzi Alfredo Hidalgo Miranda Gerardo Jimenez-Sanchez Edgar E Vallejo

Citations