• DocumentCode
    2075672
  • Title

    Protein structure prediction using hybrid AI methods

  • Author

    Guan, X. ; Mural, R.J. ; Uberbacher, E.C.

  • Author_Institution
    Div. of Eng. Phys. & Math., Oak Ridge Nat. Lab., TN, USA
  • fYear
    1994
  • fDate
    1-4 Mar 1994
  • Firstpage
    471
  • Lastpage
    473
  • Abstract
    Describes a new approach for predicting protein structures based on artificial intelligence methods and genetic algorithms. We combine nearest neighbor searching algorithms, neural networks, heuristic rules and genetic algorithms to form an integrated system to predict protein structures from their primary amino acid sequences. First, we describe our methods and how they are integrated, and then apply our methods to several protein sequences. The results are very close to the real structures obtained by crystallography. Parallel genetic algorithms are also implemented
  • Keywords
    artificial intelligence; biology computing; genetic algorithms; macromolecular configurations; physics computing; proteins; search problems; C language; artificial intelligence; computational biology; crystallography; genetic algorithms; heuristic rules; hybrid AI methods; nearest neighbor searching algorithms; neural networks; parallel algorithms; primary amino acid sequences; protein folding; protein structure prediction; searching; Amino acids; Artificial intelligence; Artificial neural networks; DNA; Genetic algorithms; Genomics; Humans; Protein engineering; Protein sequence; Spine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence for Applications, 1994., Proceedings of the Tenth Conference on
  • Conference_Location
    San Antonia, TX
  • Print_ISBN
    0-8186-5550-X
  • Type

    conf

  • DOI
    10.1109/CAIA.1994.323633
  • Filename
    323633