• DocumentCode
    3403018
  • Title

    Subcellular localization prediction of apoptosis proteins based on the data mining for amino acid index database

  • Author

    Zhuo-xing Shi ; Qi Dai ; Ping-an He ; Yu-hua Yao ; Bo Liao

  • Author_Institution
    Coll. of Life Sci., Zhejiang Sci-Tech Univ., Hangzhou, China
  • fYear
    2013
  • fDate
    23-25 Aug. 2013
  • Firstpage
    43
  • Lastpage
    48
  • Abstract
    In this work, based on the ACF model and the SVM classifier, succeeded on trials mining information that it´s more effective to analyze the subcellular localization prediction of apoptosis proteins when adopting hydrophobicity property. This information is obtained in three benchmark datasets by using the ACF model and SVM to scan the AAindex database, which contains 544 kinds of amino acids. The contribution of this work is that it first did a comprehensive research on the effectiveness of the amino acid index for the subcellular localization of apoptosis proteins.
  • Keywords
    bioinformatics; cellular biophysics; data mining; hydrophobicity; molecular biophysics; proteins; support vector machines; ACF model; SVM classifier; amino acid index database; data mining; hydrophobicity property; protein apoptosis; subcellular localization prediction; support vector machine; Accuracy; Amino acids; Indexes; Principal component analysis; Proteins; Support vector machines; Amino acid index; Apoptosis protein; Database mining; Subcellular localization prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems Biology (ISB), 2013 7th International Conference on
  • Conference_Location
    Huangshan
  • ISSN
    2325-0704
  • Type

    conf

  • DOI
    10.1109/ISB.2013.6623792
  • Filename
    6623792