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Dwivedi, Ashok Kumar
- Genome-Scale Classification of Recombinant and Non-Recombinant HIV-1 Sequences Using Artificial Neural Network Ensembles
Abstract Views :246 |
PDF Views:76
Authors
Affiliations
1 Department of Mathematics, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology, Bhopal 462 003, IN
1 Department of Mathematics, Bioinformatics and Computer Applications, Maulana Azad National Institute of Technology, Bhopal 462 003, IN
Source
Current Science, Vol 111, No 5 (2016), Pagination: 853-860Abstract
Genetic recombination and high rate of mutations in the HIV-1 genome increase the diversity of HIV-1, which allows viruses to escape more easily from host immune system or develop resistance for antiretroviral drugs. Consequently, it is indispensable to devise an effective method for recognition of recombination in HIV-1 strains. This article presents ensemble models of artificial neural network for the classification of recombinant and non-recombinant sequences of HIV-1 genome. We have evaluated the performance of these ensemble models using different classification measurements like specificity, sensitivity and classification accuracy. Furthermore, model performance was measured on receiver operating curve and using calibration graph. High classification accuracy up to 93.43% was achieved on tenfold cross validation.Keywords
Artificial Neural Network, Bagging, Boosting, Ensemble, HIV-1 Genome.Full Text
- Multilayer Perceptron and Evolutionary Radial Basis Function Neural Network Models for Discrimination of HIV-1 Genomes
Abstract Views :217 |
PDF Views:76
Authors
Affiliations
1 Mathematics and Computer Applications, Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal 462 003, IN
1 Mathematics and Computer Applications, Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal 462 003, IN
Source
Current Science, Vol 115, No 11 (2018), Pagination: 2063-2070Abstract
High rate of mutation and frequent recombination cause evolution of HIV-1 very diverse and adaptive. Revealing the recombination patterns in HIV-1 is a computationally intensive problem. Techniques based on phylogenetic analysis are not suitable for genomelevel studies. Here we elucidate approaches based on multilayer perceptron and evolutionary radial basis function neural network for the analysis of 4130 HIV- 1 genomes. These techniques show remarkable improvement over other machine learning techniques used for such classification. The models outperformed other machine learning models having 92% classification accuracy. Multilayer perceptron achieved sensitivity and specificity of 82% and 96%, whereas radial basis function neural network achieved sensitivity and specificity of 78% and 98% on tenfold cross-validation respectively.Keywords
Artificial Neural Network, HIV-1 Genome, Machine Learning, Multilayer Perceptron.Full Text
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