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SVM Model for Amino Acid Composition Based Prediction of Mycobacterium tuberculosis | OMICS International | Abstract
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
Open Access

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Research Article

SVM Model for Amino Acid Composition Based Prediction of Mycobacterium tuberculosis

Lakshmi Pillai1*, Bhasker Pant1, Usha chauhan2 and KR Pardasani3

1Phd Student, Department of Mathematics, Maulana Azad National Institute of Technology

2Assistant Professor, Department of Mathematics, Maulana Azad National Institute of Technology

3Professor, Department of Mathematics, Maulana Azad National Institute of Technology

*Corresponding Author:
Dr. Lakshmi Pillai
Department of Mathematiics
Maulana Azad National Institute of Technology
Bhopal, India
Tel: +91 (0)7828013946
E-mail: [email protected]

Received date: May 12, 2011; Accepted date: July 30, 2011; Published date: July 31, 2011

Citation: Pillai L, Pant B, Chauhan U, Pardasani KR (2011) SVM Model for Amino Acid Composition Based Prediction of Mycobacterium tuberculosis. J Comput Sci Syst Biol 4:047-049 doi:10.4172/jcsb.1000075

Copyright: © 2011 Pillai L, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

The Tuberculosis is the classical human mycobacterial disease, caused by Mycobacterium tuberculosis. The disease primarily affect the lung and causes pulmonary tuberculosis, as well as affect intestine, bone, joints, meninges, lymph nodes, skin and other tissue of the body, causing extra pulmonary tuberculosis. Thus there arises the need to understand the relationships among various parameters of these proteins for prediction of their classes, structures and functionality. The computational approaches for prediction of their classes are fast and economical therefore can be used to complement the existing wet lab techniques. Realizing their importance, in this paper an attempt has been made to correlate them with their amino acid composition and predict them with fair accuracy. This is a novel method where Mycobacterium Tuberculosis has been classified on the basis of amino acid composition using Support Vector Machine. The SVM has been implemented using SVM Light package. The method discriminates different strains of Mycobacterium Tuberculosis. The performance of the method was evaluated using 10-fold cross-validation where accuracy of 100% was obtained.

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