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iMulti-HumPhos: A Multi-Label Classifier for Identifying Human Phosphorylated Proteins Using Multiple Kernel Learning Based Support Vector Machine

Abstract

Protein phosphorylation shows a potential role in regulating protein conformation and functions. As a result, identifying an uncharacterized protein sequence as phosphorylated protein is a very meaningful problem and an urgent issue for both basic research and drug development. Although various types of computational methods have been developed for identifying the phosphorylation sites for a recognized phosphorylated protein, a very few computational methods has been developed to identify whether an uncharacterized protein can be phosphorylated or not. Therefore, there exist some scopes for making further improvement to characterize a protein as phosphorylated or not. Again, among all residues of protein molecules, three types of amino acid residues, namely serine, threonine, and tyrosine, have been found susceptible to phosphorylation which leads to the requirement of multi-label phosphorylated protein identification. Therefore, in this study, a novel computational tool termed iMulti-HumPhos has been developed to predict multi-label phosphorylated proteins by (1) extracting three different set of features from protein sequences, (2) defining an individual kernel for each set of features and combining them into a single kernel using multiple kernel learning, (3) constructing a multi-label predictor using a combination of support vector machine (SVM) where each SVM has been trained with the combined kernel. In addition, we have balanced the effect of skewed training dataset by Different Error Costs method in the development of our system. The experimental results show that the iMulti-HumPhos predictor provides significantly better performance than the existing predictor Multi-iPPseEvo. A user-friendly web server of iMulti-HumPhos is available at http://research.ru.ac.bd/iMulti-HumPhos/

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Publication details

The article was received on 24 Mar 2017, accepted on 15 Jun 2017 and first published on 16 Jun 2017


Article type: Paper
DOI: 10.1039/C7MB00180K
Citation: Mol. BioSyst., 2017, Accepted Manuscript
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    iMulti-HumPhos: A Multi-Label Classifier for Identifying Human Phosphorylated Proteins Using Multiple Kernel Learning Based Support Vector Machine

    Md. A. M. Hasan, S. Ahmad and Md. K. I. Molla, Mol. BioSyst., 2017, Accepted Manuscript , DOI: 10.1039/C7MB00180K

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