Protein folding classification by committee SVM array

Mika Takata, Yasuo Matsuyama

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    Protein folding classification is a meaningful step to improve analysis of the whole structures. We have designed committee Support Vector Machines (committee SVMs) and their array (committee SVM array) for the prediction of the folding classes. Learning and test data are amino acid sequences drawn from SCOP (Structure Classification Of Protein database). The classification category is compatible with the SCOP. SVMs and committee SVMs are designed in an one-versus-others style both for chemical data and sliding window patterns (spectrum kernels). This generates the committee SVM array. Classification performances are measured in view of the Receiver Operating Characteristic curves (ROC). Superiority of the committee SVM array to existing prediction methods is obtained through extensive experiments to compute the ROCs.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Pages369-377
    Number of pages9
    Volume5507 LNCS
    EditionPART 2
    DOIs
    Publication statusPublished - 2009
    Event15th International Conference on Neuro-Information Processing, ICONIP 2008 - Auckland
    Duration: 2008 Nov 252008 Nov 28

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 2
    Volume5507 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other15th International Conference on Neuro-Information Processing, ICONIP 2008
    CityAuckland
    Period08/11/2508/11/28

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

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  • Cite this

    Takata, M., & Matsuyama, Y. (2009). Protein folding classification by committee SVM array. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 5507 LNCS, pp. 369-377). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5507 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-03040-6_45