| Literature DB >> 22732690 |
Shaini Joseph1, Shreyas Karnik, Pravin Nilawe, V K Jayaraman, Susan Idicula-Thomas.
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/.Entities:
Mesh：
Substances：
Year: 2012 PMID： 22732690 DOI： 10.1109/TCBB.2012.89
Source DB: PubMed Journal: IEEE/ACM Trans Comput Biol Bioinform ISSN： 1545-5963 Impact factor: 3.710