©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.
Online Journal of Bioinformatics ©
Volume 18(2):53-57, 2017.
Machine learning models for evaluation of domain based classification of AIDS HIV-1 groups
Dr. Anubha Dubey.
Independent researcher & Analyst Bioinformatics, E-mail: Gayatri Nagar KATNI, M.P.INDIA Assisted by Department of Biotechnology, Bioinformatics Infra Structures, MANIT, Bhopal
Dr. Anubha Dubey, Machine learning models for evaluation of domain based classification of AIDS HIV-1 groups, Onl J Bioinform 18(2):53-57, 2017. HIV-1 evolves through rapid accumulation of mutations and recombination which actively contribute to its genetic diversity producing many groups, types and subtypes, This is similar to protein domain sequences and structures that evolve, function and exist independently from the rest of the protein chain. Each domain forms a compact 3D structure which is independently stable and folded. One protein may appear in a variety of evolutionarily related proteins. Software and methods such as SVM, HMM and Neural Networks for prediction of domains generate different results and accuracy for the same input. We describe a machine learning model for classifying HIV 1 M, N, O group domains. The HIV-1 domain based classification model was developed using Uniprot database as input for SBASE, SMART, NCBI Conserved Domain, Scan Prosite and Phylodome with J48, Bayes Net, Naive Bayes and Bagging algorithms. Results showed that SBASE predicted 98.59% and other programs 95.07-97.18% domains.
Key words: Bagging, J48, Bayes Net, Naive bayes.