Analysis and Classification of Hiv-1 Sub- Type Viruses by AR Model through Artificial Neural Networks

Authors: O. Yavuz, L. Ozyilmaz

Abstract:

HIV-1 genome is highly heterogeneous. Due to this variation, features of HIV-I genome is in a wide range. For this reason, the ability to infection of the virus changes depending on different chemokine receptors. From this point of view, R5 HIV viruses use CCR5 coreceptor while X4 viruses use CXCR5 and R5X4 viruses can utilize both coreceptors. Recently, in Bioinformatics, R5X4 viruses have been studied to classify by using the experiments on HIV-1 genome. In this study, R5X4 type of HIV viruses were classified using Auto Regressive (AR) model through Artificial Neural Networks (ANNs). The statistical data of R5X4, R5 and X4 viruses was analyzed by using signal processing methods and ANNs. Accessible residues of these virus sequences were obtained and modeled by AR model since the dimension of residues is large and different from each other. Finally the pre-processed data was used to evolve various ANN structures for determining R5X4 viruses. Furthermore ROC analysis was applied to ANNs to show their real performances. The results indicate that R5X4 viruses successfully classified with high sensitivity and specificity values training and testing ROC analysis for RBF, which gives the best performance among ANN structures.

Keywords: Auto-Regressive Model, HIV, Neural Networks, ROC Analysis.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1085052

References:

[1] E.A. Berger, P.M. Murphy, and J.M. Farber, "Chemokine Receptors as HIV-1 Coreceptors: Roles in Viral Entry, Tropism, and Disease," Ann. Rev. Immunology, vol. 17, pp. 675-700, 1999.
[2] W. Resch, N. Hoffman, and R. Swanstrom, "Improved Success of Phenotype Prediction of the Human Immunodeficiency Virus Type 1 from Envelope Variable Loop 3 Sequence Using Neural Networks," J. Virology, vol. 76, pp. 3852-3864, 2001.
[3] J.A. Loannidis, T.A. Trikalinos, and M. Law, "HIV Lipodystrophy Case Definition Using Artificial Neural Network Modeling," Antiviral Therapy, vol. 8, pp. 435-441, 2003.
[4] D. Wang and B. Larder, "Enhanced Prediction of Lopinavir Resistance from Genotype by Use of Artificial Neural Networks," J. Infectious Diseases, vol. 188, pp. 653-660, 2003.
[5] Z.L. Brumme, W.W.Y. Dong, B. Yip, B. Wynhoven, N.G. Hoffman, R. Swanstrom, M.A. Jensen, J.I. Mullins, R.S. Hogg, J.S.G. Montaner, and P.R. Harrigan, "Clinical and Immunological Impact of HIV Envelope V3 Sequence Variation after Starting Initial Triple Antiretroviral Therapy," AIDS, vol. 18, pp. F1-F9, 2004.
[6] L. Milich, B. Margolin, and R. Swanstrom, "V3 Loop of the Human Immunodeficiency Virus Type 1 Env Protein: Interpreting Sequence Variability," J. Virology, vol. 67, no. 9, pp. 5623-5634, 1993.
[7] S. Lamers, S. Beason, L. Dunlap, R. Compton, and M. Salemi, "HIVbase: A PC/Windows-Based Software Offering Storage and Querying Power for Locally Held HIV-1 Genetic, Experimental and Clinical Data," Bioinformatics, vol. 20, pp. 436-438, 2002.
[8] S. Lamers, L. Susanna, M. Salemi, M. S. McGrath and G. B. Fogel, "Prediction of R5, X4, and R5X4 HIV-1 Coreceptor Usage with Evolved Neural Networks, " Trans. On Computational Biology and Bioinformatics, Vol. 5, pp. 291-300, 2008
[9] E. Sitbon, and S. Pietrokovski, "Occurrence of protein structure elements in conserved sequence regions," BMC Structural Biology, vol. 7, 2007.
[10] R. Kong, C. X. Wang, X. H. Ma, J. H. Liu, and W. Z. Chen, "Peptides Design Based on the Interfacial Helix of Integrase Dimer, " 27th Annual Int. Conf. of the Engineering in Medicine and Biology Society, pp. 4743- 4746 2005.
[11] H. Zhou, and H.Yan, "Autoregressive Models for Spectral Analysis of Short Tandem Repeats in DNA Sequences," IEEE Int. Conf. on Systems, Man and Cybernetics, vol. 2, pp. 1286-1290, 2006.
[12] M. Akhtar, E. Ambikairajah, and J. Epps, "Detection of period-3 behavior in genomic sequences using singular value decomposition," Proc. of International Conference on Emerging Technologies, pp. 13-17, 2007
[13] G. Rosen. "Comparison of Autoregressive Measures for DNA Sequence Similarity" IEEE Genomic Signal Processing and Statistics Workshop (GENSIPS) pp. 13-17 2007.
[14] S. Haykin, Adaptive Filter Theory, Prentice-Hall, New Jersey, 2002.
[15] A. Sboner, C. Eccher, E. Blanzieri, P. Bauer, M. Cristofolini, G. Zumiani, and S. Forti, "A multiple classifier system for early melanoma diagnosis," AI in Medicine, Vol. 27, pp. 29-44, 2003.
[16] R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection". Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence vol. 2 pp. 1137-1143, 1995.