• DocumentCode
    2681562
  • Title

    Diagnostic and Therapy Prediction in Breast Cancer by Consistent Knowledge Discovery

  • Author

    Doncescu, Andrei ; Richard, Gilles ; Farmer, Muhamed

  • Author_Institution
    LAAS-CNRS, Univ. of Toulouse, Toulouse
  • fYear
    2008
  • fDate
    4-7 March 2008
  • Firstpage
    286
  • Lastpage
    292
  • Abstract
    Cells are able to use the bottom-up information (genome versus metabolic pathway) but also top-down environment/gene mutation. Our approach considers the information emergency from the sub-systems to the whole system. In these conditions an unified analytical model is very difficult to build up. Therefore an abstract model can be very helpful for prognosis diagnostic in medical science. We enhance the pertinence analysis of the information which is primordially to find out relationship between experimental data and biological knowledge.
  • Keywords
    cancer; data mining; genetics; medical computing; patient diagnosis; breast cancer; diagnostic prediction; gene mutation; knowledge discovery; medical science; pertinence analysis; prognosis diagnostic; therapy prediction; top-down environment; Bioinformatics; Breast cancer; Clustering algorithms; Clustering methods; Hidden Markov models; Learning systems; Logic programming; Medical diagnostic imaging; Medical treatment; Testing; breast cancer; fuzzy logic; inductive logic programming;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Complex, Intelligent and Software Intensive Systems, 2008. CISIS 2008. International Conference on
  • Conference_Location
    Barcelona
  • Print_ISBN
    978-0-7695-3109-0
  • Type

    conf

  • DOI
    10.1109/CISIS.2008.147
  • Filename
    4606694