• DocumentCode
    3071812
  • Title

    Epistasy Search in Population-Based Gene Mapping Using Mutual Information

  • Author

    Saraee, Mohammad ; Nikoofar, Hamidreza ; Manzour, Amir

  • Author_Institution
    Univ. of Salford, Manchester
  • fYear
    2007
  • fDate
    15-18 Dec. 2007
  • Firstpage
    621
  • Lastpage
    625
  • Abstract
    Gene mapping intends to identify the causal genetic regions of a specific phenotype mostly a complex disease. These diseases are believed to have multiple contributing loci that are potentially unknown and often have subtle patterns making them hard to find. Shannon´s mutual information figure is used as a criterion. Algorithms based on this criterion as presented and discussed. Furthermore, an algorithm is proposed to form relevance chains. The proposed algorithms are especially in favor of diseases having almost equally contributing regions known as being epistatic and is applied to both simulated and real data. AMD disease results are included. Some highly associated markers are found in AMD. C# source files for relevance-chains are freely available at https://www. sharemation. com/amanzour.
  • Keywords
    data mining; diseases; genetics; information theory; medical computing; AMD disease; Shannon mutual information; causal genetic region identification; epistasy search; pattern discovery; population-based gene mapping; relevance chains; Databases; Diseases; Entropy; Genetic communication; Information technology; Information theory; Mutual information; Signal mapping; Signal processing; Signal processing algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Information Technology, 2007 IEEE International Symposium on
  • Conference_Location
    Giza
  • Print_ISBN
    978-1-4244-1835-0
  • Electronic_ISBN
    978-1-4244-1835-0
  • Type

    conf

  • DOI
    10.1109/ISSPIT.2007.4458203
  • Filename
    4458203