Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 9, Issue 5, Pages 1535-1538Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2012.89
Keywords
Antibacterial; antifungal; antimicrobial; antiviral; prediction algorithm; random forests; SVM
Categories
Funding
- 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
Ask authors/readers for more resources
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/.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available
Share Paper
