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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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