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OJBTM
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)
ABSTRACT
Dubey A, Chouhan U., Machine learning models to classify HIV membrane
proteins, Online 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.