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ClassAMP: A Prediction Tool for Classification of Antimicrobial Peptides
- 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/.
- Subjects :
- Antifungal
Antiinfective agent
Support Vector Machine
Drug discovery
medicine.drug_class
Applied Mathematics
Antimicrobial peptides
Computational biology
Biology
Bioinformatics
Antimicrobial
Random forest
Support vector machine
Protein sequencing
Anti-Infective Agents
Genetics
medicine
Peptides
Algorithms
Biotechnology
Subjects
Details
- ISSN :
- 15455963
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Accession number :
- edsair.doi.dedup.....3e00b9dea5e1e2737a6b925ab3918fb2