• DocumentCode
    2785408
  • Title

    Research of protein structure classification based on rough set and support vector machine

  • Author

    Jian, Wang ; Jian-Ping, Li

  • Author_Institution
    Sch. of Comput. & Inf. Sci., Neijiang Normal Univ., Neijiang, China
  • fYear
    2009
  • fDate
    23-25 Oct. 2009
  • Firstpage
    124
  • Lastpage
    127
  • Abstract
    A novel method of feature extraction form protein sequences, structures and physicochemical properties has been proposed and obtained a better classification results by the key eigenvector obtained form knowledge reduction combined with the algorithm of support vector machine. Based on Jackknife detecting methods, the comprehensive classification results 78.3% and 90.9% for all-¿, all-ß, ¿+ß and ¿/ß have been obtained by the method of support vector machine when we tested Z277 and Z498 in database. Moreover, we found that protein physicochemical properties have a strong influence on classification precision of protein structure with Matlab. These results show that the method of feature extraction based on rough set is effective and available, the research of protein structure for support vector machine classification based on rough set is very effective.
  • Keywords
    biology computing; eigenvalues and eigenfunctions; feature extraction; pattern classification; proteins; rough set theory; sequences; support vector machines; Jackknife detecting methods; Matlab; classification precision; feature extraction; key eigenvector; knowledge reduction; physicochemical property; protein sequences; protein structure classification; rough set; support vector machine; Amino acids; Feature extraction; Information science; Physics computing; Protein engineering; Protein sequence; Spatial databases; Support vector machine classification; Support vector machines; Testing; Classification of protein structure; Feature extraction; Rough set; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Apperceiving Computing and Intelligence Analysis, 2009. ICACIA 2009. International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-5204-0
  • Electronic_ISBN
    978-1-4244-5206-4
  • Type

    conf

  • DOI
    10.1109/ICACIA.2009.5361135
  • Filename
    5361135