Evaluation of Parameter Contribution to Neural Network Size and Fitness in ATHENA for Genetic Analysis

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InCollection{Li:2013:GPTP,
  author =       "Ruowang Li and Emily R. Holzinger and 
                 Scott M. Dudek and Marylyn D. Ritchie",
  title =        "Evaluation of Parameter Contribution to Neural Network
                 Size and Fitness in ATHENA for Genetic Analysis",
  booktitle =    "Genetic Programming Theory and Practice XI",
  year =         "2013",
  series =       "Genetic and Evolutionary Computation",
  editor =       "Rick Riolo and Jason H. Moore and Mark Kotanchek",
  publisher =    "Springer",
  chapter =      "12",
  pages =        "211--224",
  address =      "Ann Arbor, USA",
  month =        "9-11 " # may,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Neural networks, Data mining, Human
                 genetics, Systems biology, XOR model",
  isbn13 =       "978-1-4939-0374-0",
  DOI =          "doi:10.1007/978-1-4939-0375-7_12",
  abstract =     "The vast amount of available genomics data provides us
                 an unprecedented ability to survey the entire genome
                 and search for the genetic determinants of complex
                 diseases. Until now, Genome-wide association studies
                 have been the predominant method to associate DNA
                 variations to disease traits. GWAS have successfully
                 uncovered many genetic variants associated with complex
                 diseases when the effect loci are strongly associated
                 with the trait. However, methods for studying
                 interaction effects among multiple loci are still
                 lacking. Established machine learning methods such as
                 the grammatical evolution neural networks (GENN) can be
                 adapted to help us uncover the missing interaction
                 effects that are not captured by GWAS studies. We used
                 an implementation of GENN distributed in the software
                 package ATHENA (Analysis Tool for Heritable and
                 Environmental Network Associations) to investigate the
                 effects of multiple GENN parameters and data noise
                 levels on model detection and network structure. We
                 concluded that the models produced by GENN were greatly
                 affected by algorithm parameters and data noise levels.
                 We also produced complex, multi-layer networks that
                 were not produced in the previous study. In summary,
                 GENN can produce complex, multi-layered networks when
                 the data require it for higher fitness and when the
                 parameter settings allow for a wide search of the
                 complex model space.",
  notes =        "http://cscs.umich.edu/gptp-workshops/

                 Part of \cite{Riolo:2013:GPTP} published after the
                 workshop in 2013",
}

Genetic Programming entries for Ruowang Li Emily Rose Holzinger Scott M Dudek Marylyn D Ritchie

Citations