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ClassAMP: A Prediction Tool for Classification of Antimicrobial Peptides

Authors :
Susan Idicula-Thomas
Shreyas Karnik
Shaini Joseph
Pravin Nilawe
V. K. Jayaraman
Source :
IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9:1535-1538
Publication Year :
2012
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2012.

Abstract

Antimicrobial peptides (AMPs) are gaining popularity as anti-infective agents. Information on sequence features that contribute to target specificity of AMPs will aid in accelerating drug discovery programs involving them. In this study, an algorithm called ClassAMP using Random Forests (RFs) and Support Vector Machines (SVMs) has been developed to predict the propensity of a protein sequence to have antibacterial, antifungal, or antiviral activity. ClassAMP is available at http://www.bicnirrh.res.in/classamp/.

Details

ISSN :
15455963
Volume :
9
Database :
OpenAIRE
Journal :
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Accession number :
edsair.doi.dedup.....3e00b9dea5e1e2737a6b925ab3918fb2