• DocumentCode
    1653861
  • Title

    Clustering of physico-chemical properties of amino acids

  • Author

    Falcone, Jean-Luc ; Albuquerque, Paul

  • Author_Institution
    Departement d´´Informatique, Univ. de Geneve, Switzerland
  • Volume
    4
  • fYear
    2004
  • Firstpage
    1881
  • Abstract
    The data obtained from the sequencing of organisms necessitates the perfecting of methods of predicting the function and structure of proteins. For these methods to be effective, they must be based on the physico-chemical properties of the 20 amino acids (mass, charge, etc.) of which there are currently 484. Many of these properties are very strongly correlated. To regroup them, a measurement derived from the correlation is proposed. The physico-chemical properties are classified according to this distance, using the k-means and global k-means methods. A quality index has enabled the determination of of the ´natural´ numbers of the groups. The results of the aggregations conform to the biochemical expectations. The centres of the groups thus produced constitute a new group of properties.
  • Keywords
    correlation methods; medical computing; pattern clustering; proteins; amino acid physico-chemical properties clustering; amino acids; charge; correlation; data clustering; global k-means method; mass; protein function prediction; protein structure prediction; quality index; sequencing data; Proteins; Tires;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical and Computer Engineering, 2004. Canadian Conference on
  • ISSN
    0840-7789
  • Print_ISBN
    0-7803-8253-6
  • Type

    conf

  • DOI
    10.1109/CCECE.2004.1347577
  • Filename
    1347577