Petri net modeling of high-order genetic systems using grammatical evolution

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@Article{moore:2003:BS,
  author =       "Jason H. Moore and Lance W. Hahn",
  title =        "Petri net modeling of high-order genetic systems using
                 grammatical evolution",
  journal =      "BioSystems",
  year =         "2003",
  volume =       "72",
  number =       "1-2",
  pages =        "177--186",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  DOI =          "doi:10.1016/S0303-2647(03)00142-4",
  abstract =     "Understanding how DNA sequence variations impact human
                 health through a hierarchy of biochemical and
                 physiological systems is expected to improve the
                 diagnosis, prevention, and treatment of common, complex
                 human diseases. We have previously developed a
                 hierarchical dynamic systems approach based on Petri
                 nets for generating biochemical network models that are
                 consistent with genetic models of disease
                 susceptibility. This modeling approach uses an
                 evolutionary computation approach called grammatical
                 evolution as a search strategy for optimal Petri net
                 models. We have previously demonstrated that this
                 approach routinely identifies biochemical network
                 models that are consistent with a variety of genetic
                 models in which disease susceptibility is determined by
                 nonlinear interactions between two DNA sequence
                 variations. In the present study, we evaluate whether
                 the Petri net approach is capable of identifying
                 biochemical networks that are consistent with disease
                 susceptibility due to higher order nonlinear
                 interactions between three DNA sequence variations. The
                 results indicate that our model-building approach is
                 capable of routinely identifying good, but not perfect,
                 Petri net models. Ideas for improving the algorithm for
                 this high-dimensional problem are presented.",
  notes =        "PMID: 14642666 [PubMed - indexed for MEDLINE]
                 http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Abstract&list_uids=14642666",
}

Genetic Programming entries for Jason H Moore Lance W Hahn

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