Protein folding classification by committee SVM array

Mika Takata*, Yasuo Matsuyama

*この研究の対応する著者

    研究成果: Conference contribution

    3 被引用数 (Scopus)

    抄録

    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.

    本文言語English
    ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    ページ369-377
    ページ数9
    5507 LNCS
    PART 2
    DOI
    出版ステータスPublished - 2009
    イベント15th International Conference on Neuro-Information Processing, ICONIP 2008 - Auckland
    継続期間: 2008 11 252008 11 28

    出版物シリーズ

    名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    番号PART 2
    5507 LNCS
    ISSN(印刷版)03029743
    ISSN(電子版)16113349

    Other

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

    ASJC Scopus subject areas

    • コンピュータ サイエンス(全般)
    • 理論的コンピュータサイエンス

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