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Issue No.05 - Sept.-Oct. (2012 vol.9)
pp: 1535-1538
Shaini Joseph , Biomed. Inf. Center of Indian Council of Med. Res., Nat. Inst. for Res. in Reproductive Health, Mumbai, India
Shreyas Karnik , Sch. of Inf., Indiana Univ., Marshfield, WI, USA
Pravin Nilawe , Biomed. Inf. Center of Indian Council of Med. Res., Nat. Inst. for Res. in Reproductive Health, Navi Mumbai, India
V. K. Jayaraman , Center for Dev. of Adv. Comput., Pune Univ. Campus, Pune, India
Susan Idicula-Thomas , Biomed. Inf. Center of Indian Council of Med. Res., Nat. Inst. for Res. in Reproductive Health, Mumbai, India
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
support vector machines, antibacterial activity, biology computing, drugs, molecular biophysics, proteins, random sequences, antiviral activity, ClassAMP, antimicrobial peptide classification, antiinfective agents, sequence features, drug discovery programs, random forests, support vector machines, SVM, protein sequence, antibacterial activity, antifungal activity, Anti-bacterial, Support vector machines, Anti-fungal, Radio frequency, Peptides, Predictive models, Training, SVM., Antibacterial, antifungal, antimicrobial, antiviral, prediction algorithm, random forests
Shaini Joseph, Shreyas Karnik, Pravin Nilawe, V. K. Jayaraman, Susan Idicula-Thomas, "ClassAMP: A Prediction Tool for Classification of Antimicrobial Peptides", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 5, pp. 1535-1538, Sept.-Oct. 2012, doi:10.1109/TCBB.2012.89
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