SVM model for amino acid composition based classification of HIV-1 groups

HIV is human immunodeficiency virus causes AIDS (acquired immunodeficiency syndrome) which leads to life threatening opportunistic infections. HIV-1 has three groups M, N, O known worldwide. Group M is widely distributed as it has nine subtypes and circulating recombinant forms are also developed due to rapid recombination and mutation. They play an important role in diagnosing the correct group of HIV-1. Thus there arises the need to understand the relationships among various parameters of the proteins of HIV-1 M, N, and O for prediction of their classes, structures and functionality. The overlapping patterns in the three groups lead to uncertainty in prediction of groups and thus pose challenges for development of computational for prediction of classes with fair accuracy. 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. The SVM has been implemented using Lib SVM package. The method discriminates MN, NO, MO from MNO using amino acid composition. The performance of the method was evaluated using 10-fold cross-validation where accuracy of 99.93% was obtained for MNO, accuracy three groups MN, NO, MO was 88.64%, 89.02%, 96.11% respectively.