• DocumentCode
    1164386
  • Title

    Computational functional genomics

  • Author

    Liang, Mike P. ; Troyanskaya, Olga G. ; Laederach, Alain ; Brutlag, Douglas L. ; Altman, Russ B.

  • Author_Institution
    Dept. of Genetics, Stanford Univ. Medical Center, CA, USA
  • Volume
    21
  • Issue
    6
  • fYear
    2004
  • Firstpage
    62
  • Lastpage
    69
  • Abstract
    The exponential growth of the publicly available data has transformed biology into an information rich science that provides new and interesting applications for the machine learning community. In this article, the author presents some specific examples regarding the possibility of representing biological data in a machine-learning framework as well as the contributions these representations impart to both the prediction and discovery of the biological function. The paper also illustrates the proper feature selection critical to the success of the of a particular computational functional genomics approach.
  • Keywords
    cellular biophysics; genetics; learning (artificial intelligence); molecular biophysics; proteins; biological data; cellular function identification; computational functional genomics approach; feature selection; machine-learning framework; molecular function identification; protein sequence data; Bioinformatics; Biological processes; Biological system modeling; Biological systems; Biology computing; Genomics; Large-scale systems; Machine learning algorithms; Organisms; Sequences;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    1053-5888
  • Type

    jour

  • DOI
    10.1109/MSP.2004.1359143
  • Filename
    1359143