MAIN


©1996-2019 All Rights Reserved. Online Journal of Bioinformatics . You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be, reproduced or re-transmitted without the express permission of the editors. This journal satisfies the refereeing requirements (DEST) for the Higher Education Research Data Collection (Australia). Linking:To link to this page or any pages linking to this page you must link directly to this page only here rather than put up your own page.


 

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.


MAIN

 

FULL-TEXT(SUBSCRIPTION OR PURCHASE TITLE $25USD)