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

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