Title

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

Authors

Gunjan Mishra^1#, Vivek Ananth^1#, Kalpesh Shelke², Deepak Sehgal¹ & Jayaraman Valadi^1*

Affiliation

¹Shiv Nadar University, Gautam Budha Nagar, Utter Pradesh 201314, India

²Centre for Modeling and Simulation, Pune University, Pune 4110001, India

Email

valadi@gmail.com; *Corresponding author

Article Type

Prediction Model

Date

Received January 04, 2016; Accepted January 06, 2016; Published January 31, 2016

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.

Keywords

Antiviral peptides, Support vector machine, Hepatitis, Ant colony optimization, Feature selection

Abbreviations

AMP = Antimicrobial peptides, SVM = Support Vector Machines, HIV = Human Immunodeficiency Virus, AC O = Ant Colony Optimization, AHP = Anti Hepatitis Peptides

Citation

Mishra et al. Bioinformation 12(1): 12-14 (2016)
 

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics