Journal of Computer Science & Systems Biology

SVM Model for Amino Acid Composition Based Prediction of Mycobacterium tuberculosis

Abstract

Lakshmi Pillai, Bhasker Pant, Usha chauhan and KR Pardasani

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.