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International Journal of Computational Bioinformatics and In Silico Modeling
2013: Volume-2 Issue-5
ISSN: 2320-0634

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International Journal of Computational Bioinformatics and In Silico Modeling 2(5) 2013: 239-244

Protein Function Prediction Using Support Vector Machine


Arvind Kumar Tiwari1* and R B Mishra2

Department of Computer Engineering, IIT BHU, Varanasi, India .

* Corresponding Author

ABSTRACT

Protein function prediction is very important and challenging task in Bioinformatics. In this paper we have used proteins represented by a set of enzymes i.e. Oxidoreductase, Transferase, Hydrolase, Isomerase, Ligase, and Lyase, extracted from the Enzyme Commission (EC) classification to build the models. In this paper we have used Support vector machine to predict protein function which is more efficient for resolving linear and non linear classification problems. We have used protein dataset available at PDB using features such as primary structures, secondary structures , molecular weight, structural molecular weight, chain length, atom count, ligand molecular weight and residue count as training parameters and EC number as corresponding output . Here we used expert model of support vector machine, with RBF kernel function where width is 0.10 and parameter C is 10. The result in this paper using these parameters shows that the overall average accuracy is 84.07%.


Copyright © 2013 | AIZEON publishers | All rights reserved

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Citation: AK Tiwari and RB Mishra. (2013). Protein Function Prediction Using Support Vector MachineInt J Comput Bioinfo In Silico Model 2(5): 239-244

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