Nature-Inspired Algorithms for the Genetic Analysis of Epistasis in Common Human Diseases: Theoretical Assessment of Wrapper vs. Filter Approaches

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@InProceedings{Greene:2009:cec,
  author =       "Casey S. Greene and Jeff Kiralis and Jason H. Moore",
  title =        "Nature-Inspired Algorithms for the Genetic Analysis of
                 Epistasis in Common Human Diseases: Theoretical
                 Assessment of Wrapper vs. Filter Approaches",
  booktitle =    "2009 IEEE Congress on Evolutionary Computation",
  year =         "2009",
  editor =       "Andy Tyrrell",
  pages =        "800--807",
  address =      "Trondheim, Norway",
  month =        "18-21 " # may,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-2959-2",
  file =         "P153.pdf",
  DOI =          "doi:10.1109/CEC.2009.4983027",
  abstract =     "In human genetics, new technological methods allow
                 researchers to collect a wealth of information about
                 genetic variation among individuals quickly and
                 relatively inexpensively. Studies examining more than
                 one half of a million points of genetic variation are
                 the new standard. Quickly analyzing these data to
                 discover single gene effects is both feasible and often
                 done. Unfortunately as our understanding of common
                 human disease grows, we now believe it is likely that
                 an individual's risk of these common diseases is not
                 determined by simple single gene effects. Instead it
                 seems likely that risk will be determined by nonlinear
                 gene-gene interactions, also known as epistasis.
                 Unfortunately searching for these nonlinear effects
                 requires either effective search strategies or
                 exhaustive search. Previously we have employed both
                 filter and nature-inspired probabilistic search wrapper
                 approaches such as genetic programming (GP) and ant
                 colony optimization (ACO) to this problem. We have
                 discovered that for this problem, expert knowledge is
                 critical if we are to discover these interactions. Here
                 we theoretically analyze both an expert knowledge
                 filter and a simple expert-knowledge-aware wrapper. We
                 show that under certain assumptions, the filter
                 strategy leads to the highest power. Finally we discuss
                 the implications of this work for this type of problem,
                 and discuss how probabilistic search strategies which
                 outperform a filtering approach may be designed.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "CEC 2009 - A joint meeting of the IEEE, the EPS and
                 the IET. IEEE Catalog Number: CFP09ICE-CDR",
}

Genetic Programming entries for Casey S Greene Jeff Kiralis Jason H Moore

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