Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-processing

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{Moore:2012:GPTP,
  author =       "Jason H. Moore and Douglas P. Hill and 
                 Arvis Sulovari and LaCreis Kidd",
  title =        "Genetic Analysis of Prostate Cancer Using
                 Computational Evolution, Pareto-Optimization and
                 Post-processing",
  booktitle =    "Genetic Programming Theory and Practice X",
  year =         "2012",
  series =       "Genetic and Evolutionary Computation",
  editor =       "Rick Riolo and Ekaterina Vladislavleva and 
                 Marylyn D. Ritchie and Jason H. Moore",
  publisher =    "Springer",
  chapter =      "7",
  pages =        "87--101",
  address =      "Ann Arbor, USA",
  month =        "12-14 " # may,
  keywords =     "genetic algorithms, genetic programming, Computational
                 evolution, Genetic epidemiology, Epistasis, Gene-gene
                 interactions",
  isbn13 =       "978-1-4614-6845-5",
  URL =          "http://dx.doi.org/10.1007/978-1-4614-6846-2_7",
  DOI =          "doi:10.1007/978-1-4614-6846-2_7",
  abstract =     "Given infinite time, humans would progress through
                 modelling complex data in a manner that is dependent on
                 prior expert knowledge. The goal of the present study
                 is make extensions and enhancements to a computational
                 evolution system (CES) that has the ultimate objective
                 of tinkering with data as a human would. This is
                 accomplished by providing flexibility in the
                 model-building process and a meta-layer that learns how
                 to generate better models. The key to the CES system is
                 the ability to identify and exploit expert knowledge
                 from biological databases or prior analytical results.
                 Our prior results have demonstrated that CES is capable
                 of efficiently navigating these large and rugged
                 fitness landscapes toward the discovery of biologically
                 meaningful genetic models of disease. Further, we have
                 shown that the efficacy of CES is improved dramatically
                 when the system is provided with statistical or
                 biological expert knowledge. The goal of the present
                 study was to apply CES to the genetic analysis of
                 prostate cancer aggressiveness in a large sample of
                 European Americans. We introduce here the use of
                 Pareto-optimisation to help address overfitting in the
                 learning system. We further introduce a post-processing
                 step that uses hierarchical cluster analysis to
                 generate expert knowledge from the landscape of best
                 models and their predictions across patients. We find
                 that the combination of Pareto-optimization and
                 post-processing of results greatly improves the genetic
                 analysis of prostate cancer.",
  notes =        "part of \cite{Riolo:2012:GPTP} published after the
                 workshop in 2013",
}

Genetic Programming entries for Jason H Moore Douglas P Hill Arvis Sulovari La Creis Renee Kidd

Citations