• DocumentCode
    2126667
  • Title

    Application Research of Protein Structure Prediction Based Support Vector Machine

  • Author

    Wang, Bo ; Liu, Yongkui ; Yun, Jian ; Liu, Shuang

  • Author_Institution
    Coll. of Comput. Sci. & Eng., Dalian Nat. Univ., Dalian
  • fYear
    2008
  • fDate
    21-22 Dec. 2008
  • Firstpage
    581
  • Lastpage
    584
  • Abstract
    Bioinformatics techniques to protein structure prediction mostly depend on the information available in amino acid sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. Support vector machines is a good classifier to solve classification problem and the learning results possess stronger robustness. We summarise some of the recent studies adopting this SVM learning machine for prediction structure prediction are the one which used frequent profiles with evolutionary information.
  • Keywords
    bioinformatics; generalisation (artificial intelligence); learning (artificial intelligence); molecular biophysics; pattern classification; proteins; support vector machines; SVM generalization ability; amino acid sequence; bioinformatics technique; pattern classification problem; protein structure prediction; support vector machine learning; Amino acids; Application software; Bioinformatics; Computational biology; Knowledge acquisition; Machine learning; Protein engineering; Sequences; Support vector machine classification; Support vector machines; Bioinformatics; Protein Structure Prediction; Support Vector Machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Knowledge Acquisition and Modeling, 2008. KAM '08. International Symposium on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3488-6
  • Type

    conf

  • DOI
    10.1109/KAM.2008.115
  • Filename
    4732892