USP Electronic Research Repository

Exploring potential discriminatory information embedded in PSSM to enhance protein structural class prediction accuracy

Dehzangi, A. and Paliwal, K.K. and Lyons, J. and Sharma, Alokanand and Sattar, A. (2013) Exploring potential discriminatory information embedded in PSSM to enhance protein structural class prediction accuracy. In: Lecture Notes in Bioinformatics. Lecture Notes in Computer Science, 7986 . Springer Berlin Heidelberg, Switzerland , pp. 208-219. ISBN Print 9783642391583 Online 9783642391590

[img]
Preview
PDF - Published Version
Download (353Kb) | Preview

    Abstract

    Determining the structural class of a given protein can provide important information about its functionality and its general tertiary structure. In the last two decades, the protein structural class prediction problem has attracted tremendous attention and its prediction accuracy has been significantly improved. Features extracted from the Position Specific Scoring Matrix (PSSM) have played an important role to achieve this enhancement. However, this information has not been adequately explored since the protein structural class prediction accuracy relying on PSSM for feature extraction still remains limited. In this study, to explore this potential, we propose segmentation-based feature extraction technique based on the concepts of amino acids’ distribution and auto covariance. By applying a Support Vector Machine (SVM) to our extracted features, we enhance protein structural class prediction accuracy up to 16% over similar studies found in the literature. We achieve over 90% and 80% prediction accuracies for 25PDB and 1189 benchmarks respectively by solely relying on the PSSM for feature extraction.

    Item Type: Book Chapter
    Subjects: T Technology > T Technology (General)
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
    Depositing User: Alokanand Sharma
    Date Deposited: 17 Dec 2013 12:24
    Last Modified: 12 Jul 2016 14:02
    URI: http://repository.usp.ac.fj/id/eprint/7026
    UNSPECIFIED

    Actions (login required)

    View Item

    Document Downloads

    More statistics for this item...