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Online Journal of Bioinformatics ©
Volume 13(2):274-284, 2012
Machine learning models to classify HIV membrane and soluble proteins
Anubha Dubey* Dr.Usha Chouhan**
Department(s) of Bioinformatics and Mathematics, MANIT, BHOPAL (M.P)
Dubey A, Chouhan U., Machine learning models to classify HIV membrane proteins, Onl J Bioinform., 13(2):274-284, 2012. HIV protein sequences from Uniprot database and various machine learning algorithms were used to classify HIV proteins into membrane proteins and soluble proteins. Bagging, the WEKA classified with 96.9388% accuracy and transmembrane helices with Bayes net 98.9362%. Support Vector Machine based classification of HIV membrane proteins and soluble proteins on the basis of amino acid based composition resulted in 97% accuracy.
Keywords: SVM, Transmembrane, WEKA, Bayes net, Prediction.