Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Chouhan, Usha
- Genome-Scale Classification of Recombinant and Non-Recombinant HIV-1 Sequences Using Artificial Neural Network Ensembles
Abstract Views :248 |
PDF Views:80
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
- Chemometric Modelling of PPAR-α and PPAR-γ Dual Agonists for the Treatment of Type-2 Diabetes
Abstract Views :208 |
PDF Views:79
Authors
Neha Verma
1,
Usha Chouhan
1
Affiliations
1 Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal 462 003, IN
1 Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal 462 003, IN
Source
Current Science, Vol 111, No 2 (2016), Pagination: 356-367Abstract
Type-2 diabetes mellitus (T2DM) is an enervating and fast-growing disease characterized by hyperglycaemia. The increasing incidences of T2DM represent a public health problem. The disease is characterized by loss in sensitivity of tissues towards insulin, which can be restored by the activation of peroxisome proliferatoractivated receptors (PPARs). PPARs are members of the nuclear receptor family, which function as a ligand-dependent transcription factor. The aim of the present work is to develop ligands, which can activate PPARs and are expected to lower LDL cholesterol and triglycerides, raise HDL cholesterol, and normalize hyperglycaemia. Here quantitative structure-activity relationship (QSAR) study is performed, followed by pharmacophore modelling and docking of the most active compound to the proteins PPAR-γ (PDB ID: 1FM9) and PPAR-α (PDB ID : 1K7L). Docking studies revealed the importance of hydrogen-bonding interactions for the binding of targets with the ligand. QSAR study is performed on the dataset by means of multiple linear regression and partial least squares (PLS) techniques. A good correlation is found by regression analysis between the observed and predicted activities as evident by their R2 (0.651), Q2 (0.649) and R2pred (0.606) for PPAR-γ, and R2 (0.784), Q2 (0.774) and R2pred (0.841) for PPAR-α. Subsequent analysis of the model by PLS cross-validation technique yields a similar set of coefficients. Pharmacophore studies reveal the importance of features like hydrogen bond donor, hydrogen bond acceptor and aromaticity, which contribute significantly in both models and are essential for binding of ligands to the receptor and also for their proper functioning.Keywords
Chemometric Modelling, Diabetes Mellitus, Peroxisome Proliferator-Activated Receptors, Quantitative Structure–Activity Relationship.Full Text
- Multilayer Perceptron and Evolutionary Radial Basis Function Neural Network Models for Discrimination of HIV-1 Genomes
Abstract Views :219 |
PDF Views:79
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
References
- Safrit, J. T., Fast, P. E., Gieber, L., Kuipers, H., Dean, H. J. and Koff, W. C., Status of vaccine research and development of vaccines for HIV-1. Vaccine, 2016.
- Cihlar, T. and Fordyce, M., Current status and prospects of HIV treatment. Curr. Opin. Virol., 2016, 18, 50–56.
- Sharp, P. M. and Hahn, B. H., Origins of HIV and the AIDS pandemic. Cold Spring Harbor Perspect. Med., 2011, 1, a006841.
- Zanini, F., Brodin, J., Thebo, L., Lanz, C., Bratt, G., Albert, J. and Neher, R. A., Population genomics of intrapatient HIV-1 evolution. eLife, 2016, 4, e11282.
- Robertson, D. et al., HIV-1 nomenclature proposal. Science, 2000, 288, 55.
- McCutchan, F. E., Global epidemiology of HIV. J. Med. Virol., 2006, 78, S7–S12.
- Robertson, D. L., Hahn, B. H. and Sharp, P. M., Recombination in AIDS viruses. J. Mol. Evol., 1995, 40, 249–259.
- Palella Jr, F. J. et al., Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. N Engl. J. Med., 1998, 338, 853–860.
- Rambaut, A., Robertson, D. L., Pybus, O. G., Peeters, M. and Holmes, E. C., Human immunodeficiency virus: phylogeny and the origin of HIV-1. Nature, 2001, 410, 1047–1048.
- Ntemgwa, M., Gill, M. J., Brenner, B. G., Moisi, D. and Wainberg, M. A., Discrepancies in assignment of subtype/recombinant forms by genotyping programs for HIV type 1 drug resistance testing may falsely predict superinfection. AIDS Res. Hum. Retroviruses, 2008, 24, 995–1002.
- Wu, X., Cai, Z., Wan, X.-F., Hoang, T., Goebel, R. and Lin, G., Nucleotide composition string selection in HIV-1 subtyping using whole genomes. Bioinformatics, 2007, 23, 1744–1752.
- Hoelscher, M. et al., Detection of HIV-1 subtypes, recombinants, and dual infections in East Africa by a multi-region hybridization assay. AIDS, 2002, 16, 2055–2064.
- Dessimoz, C., Margadant, D. and Gonnet, G. H., DLIGHT–lateral gene transfer detection using pairwise evolutionary distances in a statistical framework. In Annual International Conference on Research in Computational Molecular Biology, Springer, Berlin, Heidelberg, 2008, pp. 315–330.
- Truszkowski, J. and Brown, D. G., More accurate recombination prediction in HIV-1 using a robust decoding algorithm for HMMS. BMC Bioinform., 2011, 12, 1.
- Daubin, V., Lerat, E. and Perrière, G., The source of laterally transferred genes in bacterial genomes. Genome Biol., 2003, 4, R57.
- Worning, P., Jensen, L. J., Nelson, K. E., Brunak, S. and Ussery, D. W., Structural analysis of DNA sequence: evidence for lateral gene transfer in Thermotoga maritima. Nucleic Acids Res., 2000, 28, 706–709.
- Lawrence, J. G. and Ochman, H., Molecular archaeology of the Escherichia coli genome. Proc. Natl. Acad. Sci. USA, 1998, 95, 9413–9417.
- Jetzt, A. E., Yu, H., Klarmann, G. J., Ron, Y., Preston, B. D. and Dougherty, J. P., High rate of recombination throughout the human immunodeficiency virus type 1 genome. J. Virol., 2000, 74, 1234–1240.
- Rozanov, M., Plikat, U., Chappey, C., Kochergin, A. and Tatusova, T., A web-based genotyping resource for viral sequences. Nucleic Acids Res., 2004, 32, W654–W659.
- Wu, X. et al., Whole genome phyogeny construction via complete composition vectors. Int. J. Bioinform. Res. Appl., 2006, 2, 219– 248.
- Thompson, K. and Charnigo, R., Parallel computing in genomewide association studies. J. Biometr. Biostat., 2015, 6(1), 1.
- Eliuk, A. S., Keith Ruiter, B. and Pierre Boulanger, C., Classifying HIV-1 circulating recombinant forms. In Proceedings of the International Conference on Bioinformatics and Computational Biology (BIOCOMP), The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (World Comp), 2011, p. 1.
- Dwivedi, A. K. and Chouhan, U., Comparative study of machine learning techniques for genome scale discrimination of recombinant HIV-1 strains. J. Med. Imaging Health Inf., 2016, 6, 425– 430.
- Briesmeister, J. F., Los Alamos National Laboratory. Oak Ridge National Laboratory, MCNP-4B Monte Carlo N-Particle Transport Code System, Manual La-12625-M, version B, 2000, 4, 1997.
- Mitchell, T. M., Machine Learning, McGraw Hill, Burr Ridge, IL, USA, 1997, 45.
- Hoptroff, R. G., The principles and practice of time series forecasting and business modelling using neural nets. Neural Comput. Appl., 1993, 1, 59–66.
- Dwivedi, A. K., Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput. Appl., 2016, 29(10), 685–693.
- Dwivedi, A. K., Artificial neural network model for effective cancer classification using microarray gene expression data. Neural Comput. Appl., 2016, 29(12), 1545–1554.
- Dwivedi, A. K. and Chouhan, U., Comparative study of artificial neural network for classification of hot and cold recombination regions in Saccharomyces cerevisiae. Neural Comput. Appl., 2016, 29(2), 529–535.
- Jones, A. J., Genetic algorithms and their applications to the design of neural networks. Neural Comput. Appl., 1993, 1, 32–45.
- Venkatesan, D., Kannan, K. and Saravanan, R., A genetic algorithmbased artificial neural network model for the optimization of machining processes. Neural Comput. Appl., 2009, 18, 135– 140.
- Radcliffe, N. J., Genetic set recombination and its application to neural network topology optimisation. Neural Comput. Appl., 1993, 1, 67–90.
- Brown, M. P. et al., Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci., USA, 2000, 97, 262–267.
- Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M. and Haussler, D., Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 2000, 16, 906–914.
- Dwivedi, A. K. and Chouhan, U., Genome-scale classification of recombinant and nonrecombinant HIV-1 sequences using artificial neural network ensembles. Curr. Sci., 2016, 111, 853–860.
- Yasdi, R., A literature survey on applications of neural networks for human–computer interaction. Neural Comput. Appl., 2000, 9, 245–258.
- Xia, X. and Xie, Z., DAMBE: software package for data analysis in molecular biology and evolution. J. Hered., 2001, 92, 371–373.
- Jenkins, G. M. and Holmes, E. C., The extent of codon usage bias in human RNA viruses and its evolutionary origin. Virus Res., 2003, 92, 1–7.
- García-Pedrajas, N., Hervás-Martínez, C. and Ortiz-Boyer, D., Cooperative coevolution of artificial neural network ensembles for pattern classification. IEEE Trans. Evol. Comput., 2005, 9, 271– 302.
- Yao, X. and Liu, Y., Making use of population information in evolutionary artificial neural networks. IEEE Trans. Syst. Man Cybern., Part B, 1998, 28, 417–425.
- Haykin, S., Neural Networks: a Comprehensive Foundation, 1994. McMillan, New Jersey, USA, 2010.
- Saha, A., Wu, C.-L. and Tang, D.-S., Approximation, dimension reduction, and nonconvex optimization using linear superpositions of Gaussians. IEEE Trans. Comput., 1993, 42, 1222–1233.
- Lowe, D. and Broomhead, D., Multivariable functional interpolation and adaptive networks. Complex Syst., 1988, 2, 321–355.
- Lee, C.-C., Chung, P.-C., Tsai, J.-R. and Chang, C.-I., Robust radial basis function neural networks. IEEE Trans. Syst., Man, Cybernetics, Part B, 1999, 29, 674–685.
- Light, W. A., Some aspects of radial basis function approximation. In Approximation Theory, Spline Functions and Applications Springer, Dordrecht, 1992, pp. 163–190.
- Rivas, V. M., Merelo, J., Castillo, P., Arenas, M. G. and Castellano, J., Evolving rbf neural networks for time-series forecasting with EVRBF. Inf. Sci., 2004, 165, 207–220.
- Broomhead, D. S. and Lowe, D., Multivariate functional interpolation and adaptive networks. Complex Syst., 1988, 2, 321–355.
- Vapnik, V., Statistical Learning Theory, Wiley New York, USA, 1998, vol. 3.