• DocumentCode
    394123
  • Title

    A method for modelling genetic regulatory networks by using evolving connectionist systems and microarray gene expression data

  • Author

    Kasabov, Nikola K. ; Dimitrov, Dimiter S.

  • Author_Institution
    Knowledge Eng. & Discovery Res. Inst., Auckland Univ. of Technol., New Zealand
  • Volume
    2
  • fYear
    2002
  • fDate
    18-22 Nov. 2002
  • Firstpage
    596
  • Abstract
    The paper describes the problem of discovering genetic networks from time course gene expression data (the reverse engineering approach) and introduces a novel method for using evolving connectionist systems (ECOS) for this task. A case study is used to illustrate the approach. Genetic regulatory networks, once constructed, can be potentially used to model the behaviour of a cell or an organism from initial conditions.
  • Keywords
    fuzzy neural nets; genetic algorithms; molecular biophysics; neurophysiology; physiological models; Zadeh Mamdani fuzzy rules; evolving connectionist systems; fuzzy neural network; genetic regulatory networks; microarray gene expression data; molecular biology; Biological system modeling; Cancer; Cells (biology); Fuzzy neural networks; Gene expression; Genetics; Knowledge engineering; Neural networks; Paper technology; Reverse engineering;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
  • Print_ISBN
    981-04-7524-1
  • Type

    conf

  • DOI
    10.1109/ICONIP.2002.1198127
  • Filename
    1198127