A Methodology for Disease Gene Association using Centrality Measures

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@InProceedings{Heravi:2016:CEC,
  author =       "Ashkan Entezari Heravi and Sheridan Houghten",
  title =        "A Methodology for Disease Gene Association using
                 Centrality Measures",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "24--31",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7743774",
  abstract =     "Disease-gene association attempts to determine which
                 genes are involved with genetic diseases. Various
                 methodologies have been applied to this problem for
                 different diseases. In earlier work, two evolutionary
                 approaches were used to analyse the complex network of
                 gene interaction. This paper presents an improvement
                 upon the genetic programming approach using a variety
                 of centrality measures to analyze the networks. This
                 approach is applied to both Parkinson's disease and
                 breast cancer.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Ashkan Entezari Heravi Sheridan Houghten

Citations