Exploiting Expert Knowledge in Genetic Programming for Genome-Wide Genetic Analysis

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

@InProceedings{Moore:PPSN:2006,
  author =       "Jason H. Moore and Bill C. White",
  title =        "Exploiting Expert Knowledge in Genetic Programming for
                 Genome-Wide Genetic Analysis",
  booktitle =    "Parallel Problem Solving from Nature - PPSN IX",
  year =         "2006",
  editor =       "Thomas Philip Runarsson and Hans-Georg Beyer and 
                 Edmund Burke and Juan J. Merelo-Guervos and 
                 L. Darrell Whitley and Xin Yao",
  volume =       "4193",
  pages =        "969--977",
  series =       "LNCS",
  address =      "Reykjavik, Iceland",
  publisher_address = "Berlin",
  month =        "9-13 " # sep,
  publisher =    "Springer-Verlag",
  ISBN =         "3-540-38990-3",
  keywords =     "genetic algorithms, genetic programming, SNP, MDR,
                 GAlib",
  URL =          "http://ppsn2006.raunvis.hi.is/proceedings/262.pdf",
  DOI =          "doi:10.1007/11844297_98",
  size =         "9 pages",
  abstract =     "Human genetics is undergoing an information explosion.
                 The availability of chip-based technology facilitates
                 the measurement of thousands of DNA sequence variation
                 from across the human genome. The challenge is to sift
                 through these high-dimensional datasets to identify
                 combinations of interacting DNA sequence variations
                 that are predictive of common diseases. The goal of
                 this paper was to develop and evaluate a genetic
                 programming (GP) approach for attribute selection and
                 modelling that uses expert knowledge such as Tuned
                 ReliefF (TuRF) scores during selection to ensure trees
                 with good building blocks are recombined and
                 reproduced. We show here that using expert knowledge to
                 select trees performs as well as a multiobjective
                 fitness function but requires only a tenth of the
                 population size. This study demonstrates that GP may be
                 a useful computational discovery tool in this domain.",
  notes =        "PPSN-IX

                 NB human's are diploid",
}

Genetic Programming entries for Jason H Moore Bill C White

Citations