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Multilayer Perceptron and Evolutionary Radial Basis Function Neural Network Models for Discrimination of HIV-1 Genomes


Affiliations
1 Mathematics and Computer Applications, Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal 462 003, India
 

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.
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  • Multilayer Perceptron and Evolutionary Radial Basis Function Neural Network Models for Discrimination of HIV-1 Genomes

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Authors

Ashok Kumar Dwivedi
Mathematics and Computer Applications, Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal 462 003, India
Usha Chouhan
Mathematics and Computer Applications, Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal 462 003, India

Abstract


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.

References





DOI: https://doi.org/10.18520/cs%2Fv115%2Fi11%2F2063-2070