Using expert knowledge in initialization for genome-wide analysis of epistasis using genetic programming

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

@InProceedings{Greene:2008:gecco,
  author =       "Casey S. Greene and Bill C. White and Jason H. Moore",
  title =        "Using expert knowledge in initialization for
                 genome-wide analysis of epistasis using genetic
                 programming",
  booktitle =    "GECCO '08: Proceedings of the 10th annual conference
                 on Genetic and evolutionary computation",
  year =         "2008",
  editor =       "Maarten Keijzer and Giuliano Antoniol and 
                 Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and 
                 Nikolaus Hansen and John H. Holmes and 
                 Gregory S. Hornby and Daniel Howard and James Kennedy and 
                 Sanjeev Kumar and Fernando G. Lobo and 
                 Julian Francis Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Jordan Pollack and Kumara Sastry and 
                 Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and 
                 Ingo Wegener",
  isbn13 =       "978-1-60558-130-9",
  pages =        "351--352",
  address =      "Atlanta, GA, USA",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p351.pdf",
  DOI =          "doi:10.1145/1389095.1389158",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "12-16 " # jul,
  abstract =     "In human genetics it is now possible to measure large
                 numbers of DNA sequence variations across the human
                 genome. Given current knowledge about biological
                 networks and disease processes it seems likely that
                 disease risk can best be modelled by interactions
                 between biological components, which may be examined as
                 interacting DNA sequence variations. The machine
                 learning challenge is to effectively explore
                 interactions in these datasets to identify combinations
                 of variations which are predictive of common human
                 diseases. Genetic programming is a promising approach
                 to this problem. The goal of this study is to examine
                 the role that an expert knowledge aware initialiser can
                 play in the framework of genetic programming. We show
                 that this expert knowledge aware initializer
                 outperforms both a random initializer and an
                 enumerative initialiser.",
  keywords =     "genetic algorithms, genetic programming, expert
                 knowledge, genetic analysis, Initialisation,
                 Bioinformatics, computational biology: Poster, TuRF,
                 Relief, SNP, MDR, SDA",
  notes =        "GECCO-2008 A joint meeting of the seventeenth
                 international conference on genetic algorithms
                 (ICGA-2008) and the thirteenth annual genetic
                 programming conference (GP-2008).

                 ACM Order Number 910081. Also known as
                 \cite{1389158}

                 Comparison of three ways of loading problem inputs
                 (10000+) into initial population to predict clinical
                 end point (death). Artificial datasets.",
}

Genetic Programming entries for Casey S Greene Bill C White Jason H Moore

Citations