• DocumentCode
    3399464
  • Title

    A genetic algorithm applied to optimal gene subset selection

  • Author

    Ding, ShengChao ; Liu, Juan ; Wu, ChahLe ; Yang, Qing

  • Author_Institution
    Sch. of Comput. Sci., Wuhan Univ., Hubei, China
  • Volume
    2
  • fYear
    2004
  • fDate
    19-23 June 2004
  • Firstpage
    1654
  • Abstract
    Optimal gene subset selection plays an important role in classification of patient samples. Different to existed methods, we propose a novel optimal gene subset selection approach based on genetic algorithms (GAs). Special fitness function is applied in this scheme. Going beyond other methods, this GA-based method automatically determines the members of a predictive gene group, as well as the optimal group size. The evaluation experiments are applied to two data sets. The results and some discussions are presented too.
  • Keywords
    biomedical imaging; genetic algorithms; image classification; learning (artificial intelligence); medical diagnostic computing; tumours; data sets; genetic algorithm; optimal gene subset selection; optimal group size; patient samples; predictive gene group; special fitness function; Cancer; Computer science; Content addressable storage; DNA; Gene expression; Genetic algorithms; Medical treatment; Microscopy; Monitoring; Neoplasms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2004. CEC2004. Congress on
  • Print_ISBN
    0-7803-8515-2
  • Type

    conf

  • DOI
    10.1109/CEC.2004.1331094
  • Filename
    1331094