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Title

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

 

Authors

Gunjan Mishra1#, Vivek Ananth1#, Kalpesh Shelke2, Deepak Sehgal1 & Jayaraman Valadi1*

 

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

 

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%. 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

 

License This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.