Classification of anti hepatitis peptides using Support Vector Machine with hybrid Ant Colony Optimization

Hepatitis is an emerging global threat to public health due to associated mortality, morbidity, cancer and HIV co-infection. Available diagnostics and therapeutics are inadequate to intercept the course and transmission of the disease. Antimicrobial peptides (AMP) are widely studied and broad-spectrum host defense peptides are investigated as a targeted anti-viral. Therefore, it is of interest to describe the supervised identification of anti-hepatitis peptides. We used a hybrid Support Vector Machine (SVM) with Ant Colony Optimization (ACO) algorithm for simultaneous classification and domain feature selection. The described model shows a 10 fold cross-validation accuracy of 94%. This is a reliable and a useful tool for the prediction and identification of hepatitis specific drug activity.