Evaluation of a discrete dynamic systems approach for modeling the hierarchical relationship between genes, biochemistry, and disease susceptibility

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@Article{Moore:2004:DCDS,
  author =       "Jason H. Moore and Lance W. Hahn",
  title =        "Evaluation of a discrete dynamic systems approach for
                 modeling the hierarchical relationship between genes,
                 biochemistry, and disease susceptibility",
  journal =      "Discrete and Continuous Dynamical Systems: Series B",
  year =         "2004",
  volume =       "4",
  number =       "1",
  pages =        "275--287",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, epistasis, gene-gene interactions, Petri
                 nets",
  ISSN =         "1531-3492",
  DOI =          "doi:10.3934/dcdsb.2004.4.275",
  abstract =     "A central goal of human genetics is the identification
                 of combinations of DNA sequence variations that
                 increase susceptibility to common, complex human
                 diseases. Our ability to use genetic information to
                 improve public health efforts to diagnose, prevent, and
                 treat common human diseases will depend on our ability
                 to understand the hierarchical relationship between
                 complex biological systems at the genetic, cellular,
                 biochemical, physiological, anatomical, and clinical
                 endpoint levels. We have previously demonstrated that
                 Petri nets are useful for building discrete dynamic
                 systems models of biochemical networks that are
                 consistent with nonlinear gene-gene interactions
                 observed in epidemiological studies. Further, we have
                 developed a machine learning approach that facilitates
                 the automatic discovery of Petri net models thus
                 eliminating the need for human-based trial and error
                 approaches. In the present study, we evaluate this
                 automated model discovery approach using four different
                 nonlinear gene-gene interaction models. The results
                 indicate that our model-building approach routinely
                 identifies accurate Petri net models in a
                 human-competitive manner. We anticipate that this
                 general modeling strategy will be useful for generating
                 hypotheses about the hierarchical relationship between
                 genes, biochemistry, and measures of human health.",
  notes =        "http://www.aimsciences.org/journals/home.jsp?journalID=2

                 2000 Mathematics Subject Classification.
                 92D30.

                 Mathematical Models in Cancer A special issue based on
                 the Cancer Workshop at Vanderbilt University 2002 Guest
                 Editors: Mary Ann Horn and Glenn Webb This special
                 issue can be ordered as a book",
}

Genetic Programming entries for Jason H Moore Lance W Hahn

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