Predicting protein structural class by SVM with class-wise optimized features and decision probabilities

Ashish Anand, Pugalenthi Ganesan, P. N. Suganthan*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    54 Scopus citations

    Abstract

    Determination of protein structural class solely from sequence information is a challenging task. Several attempts to solve this problem using various methods can be found in literature. We present support vector machine (SVM) approach where probability-based decision is used along with class-wise optimized feature sets. This approach has two distinguishing characteristics from earlier attempts: (1) it uses class-wise optimized features and (2) decisions of different SVM classifiers are coupled with probability estimates to make the final prediction. The algorithm was tested on three datasets, containing 498 domains, 1092 domains and 5261 domains. Ten-fold external cross-validation was performed to assess the performance of the algorithm. Significantly high accuracy of 92.89% was obtained for the 498-dataset. We achieved 54.67% accuracy for the dataset with 1092 domains, which is better than the previously reported best accuracy of 53.8%. We obtained 59.43% prediction accuracy for the larger and less redundant 5261-dataset. We also investigated the advantage of using class-wise features over union of these features (conventional approach) in one-vs.-all SVM framework. Our results clearly show the advantage of using class-wise optimized features. Brief analysis of the selected class-wise features indicates their biological significance.

    Original languageEnglish (US)
    Pages (from-to)375-380
    Number of pages6
    JournalJournal of Theoretical Biology
    Volume253
    Issue number2
    DOIs
    StatePublished - Jul 21 2008

    Keywords

    • Multi-class SVM
    • Probability outputs SVM
    • SCOP class classification

    ASJC Scopus subject areas

    • Statistics and Probability
    • Modeling and Simulation
    • Biochemistry, Genetics and Molecular Biology(all)
    • Immunology and Microbiology(all)
    • Agricultural and Biological Sciences(all)
    • Applied Mathematics

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