期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 9, 期 5, 页码 1535-1538出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2012.89
关键词
Antibacterial; antifungal; antimicrobial; antiviral; prediction algorithm; random forests; SVM
类别
资金
- Department of Science and Technology, Government of India [SR/S3/CE/52/2007]
- NIRRH [NIRRH/A/15/11]
- Indian Council of Medical Research [63/128/2001-BMS]
- CSIR
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/.
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