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Genetic algorithm for an optimized weighted voting scheme incorporating k-separated bigram transition probabilities to improve protein fold recognition

Saini, Harsh and Raicar, Gaurav and Lal, Sunil P. and Dehzangi, A. and Lyons, J. and Paliwal, K.K. and Imoto, S. and Miyano, S. and Sharma, Alokanand (2014) Genetic algorithm for an optimized weighted voting scheme incorporating k-separated bigram transition probabilities to improve protein fold recognition. [Conference Proceedings]

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    Abstract

    In biology, identifying the tertiary structure of a protein helps determine its functions. A step towards tertiary structure identification is predicting a protein's fold. Computational methods have been applied to determine a protein's fold by assembling information from its structural, physicochemical and/or evolutionary properties. It has been shown that evolutionary data helps improve prediction accuracy. In this study, a scheme is proposed that uses the genetic algorithm (GA) to optimize a weighted voting system to improve protein fold recognition. This scheme incorporates k-separated bigram transition probabilities for feature extraction, which are based on the Position Specific Scoring Matrix (PSSM). A set of SVM classifiers are used for initial classification, whereupon their predictions are consolidated using the optimized weighted voting system. This scheme has been demonstrated on the Ding and Dubchak (DD) benchmarked data set.

    Item Type: Conference Proceedings
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Depositing User: Harsh Saini
    Date Deposited: 16 Mar 2015 12:09
    Last Modified: 06 Jul 2016 08:55
    URI: http://repository.usp.ac.fj/id/eprint/8117
    UNSPECIFIED

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