title: Classification of anti hepatitis peptides using Support Vector Machine with hybrid Ant Colony OptimizationThe Luxembourg database of trichothecene type B F. graminearum and F. culmorum producers.
abstract: 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 percent. This is a reliable and a useful tool for the prediction and identification of hepatitis specific drug activity.
authors.affiliation: Shiv Nadar University, Gautam Budha Nagar, U.P 201314.
authors.forename: Gunjan
authors.initials: G
authors.lastname: Mishra
authors.affiliation: Shiv Nadar University, Gautam Budha Nagar, U.P 201314.
authors.forename: Vivek
authors.initials: V
authors.lastname: Ananth
authors.affiliation: Centre for Modeling and Simulation, Pune University, Pune 4110001.
authors.forename: Kalpesh
authors.initials: K
authors.lastname: Shelke
authors.affiliation: Shiv Nadar University, Gautam Budha Nagar, U.P 201314.
authors.forename: Deepak
authors.initials: D
authors.lastname: Sehgal
authors.affiliation: Shiv Nadar University, Gautam Budha Nagar, U.P 201314.
authors.forename: Jayaraman
authors.initials: J
authors.lastname: Deepak