Analysis and Classification of Hiv-1 Sub- Type Viruses by AR Model through Artificial Neural Networks
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:64439", author = "O. Yavuz and L. Ozyilmaz", title = "Analysis and Classification of Hiv-1 Sub- Type Viruses by AR Model through Artificial Neural Networks", 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.", volume = "3", number = "1", pages = "213-6", }