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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, 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.


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