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OJBTM
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: anubhadubey@rediffmail.com, Gayatri
Nagar KATNI, M.P.INDIA Assisted by Department of Biotechnology,
Bioinformatics Infra Structures, MANIT, Bhopal
ABSTRACT
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.
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