Abstract

Neural networks have been applied successfully to a broad range of fields such as finance, data mining, medicine, engineering, geology, physics and biology. In finance, neural networks have been used for stock market prediction, credit rating, bankruptcy prediction and economic indicator forecasts. In medicine, neural networks have been used extensively in medical diagnosis, detection and evaluation of medical conditions and treatment cost estimation. Furthermore, neural networks have found application in data mining projects for the purposes of prediction, classification, knowledge discovery, response modeling and time series analysis. This review paper will present the application of neural networks to the study of HIV. HIV research falls into four broad areas namely, behavioral research, diagnostic research, vaccine research and biomedical research. Most of the research publications featured in this review paper emanate from the four broad HIV research areas and will be presented in three categories namely prediction, classification and function approximation.

References

• Azam 200. Biologically Inspired Modular Neural Networks. PhD Dissertation. Virginial Polytechnic Institute and State University.
• Betechuoh B.L. et. al. 2007. Using Inverse Neural Networks for HIV Adaptive Control. International Journal of Computational Intelligence Research. vol.3, No. 1. 11-15.
• Bettebghor D. et. al. 2011. Surrogate Modeling Approximation using a Mixture of Experts based on EM joint Estimation. Structural and Multidisciplinary Optimization. Vol. 43, No. 2, 243-259.
• Bitzer S. and Kiebel J. 2012. Recognizing Recurrent Neural Networks (rRNN): Bayesian Inference for Recurrent Neural Networks. arXiv: 1201, 4339v1.
• Calcagno G. and Staiano Antonino. 2010. A Multilayer Neural Network-based approach for the identification of responsiveness to interferon therapy in multiple sclerosis patients. Information Sciences. Vol. 180, Issue 21, 4153-4163.
• Deeb O. and Jawabreh M. 2012. Exploring QSARs for Inhibitory Activity of Cyclic Urea and Nonpeptide-Cyclic Cyanoguanidine Derivatives HIV-1 Protease Inhibitors by Artificial Neural Network. Advances in Chemical Engineering and Science, vol. 2, 82-100.
• Dong Z. et. al. 2011. Injection Material Selection Method based on Optimizing Neural Network. Advances in Intelligent and Soft Computing. Vol. 104, 339-344.
• Goodarzi M. and Freitas M.P. 2010. MIA–QSAR coupled to principal component analysis-adaptive neuro-fuzzy inference systems (PCA–ANFIS) for the modeling of the anti-HIV reverse transcriptase activities of TIBO derivatives. European Journal of Medicinal Chemistry vol. 45, 1352–1358
• Graben P.B. and Wright J. 2011. From McCulloch–Pitts Neurons Toward Biology. Society for Mathematical Biology.
• Hatzakis G.E. and Tsoukas C.M. 2002. Neural networks morbidity and mortality modeling during loss of HIV T-cell homeostasis. Proc AMIA Symp. 320–324.
• Hatzakis G.E. et al. 2001. Neural networks in the assessment of HIV immunopathology. Proc AMIA Symp. 249–253.
• Hatzakis G.E. et. al. 2005. Neural Network-longitudinal assessment of the Electronic Anti-Retroviral THerapy (EARTH) Cohort to follow response to HIV-treatment. AMIA Annu Symp Proc. 301–305.
• Herman R.A. et. al. 1999. Comparison of a Neural Network Approach with Five Traditional Methods for Predicting Creatinine Clearance in Patients with Human Immunodeficiency Virus Infection. PHARMACOTHERAPY vol. 19, No. 6.
• Kwak N. K. and Lee C. 1997. A neural network application to classification of health status of HIV/AIDS patients. J. Med. Syst. 21(2):87–97.
• Lamers S.L. et. al. 2008. Prediction of R5, X4, and R5X4 HIV-1 Coreceptor Usage with Evolved Neural Networks. IEEE/ACM transactions on computational biology and bioinformatics. vol. 5. No. 2.
• Larder et. al. 2009. Application of Artificial Neural Networks for Decision Support in Medicine. Methods in Molecular Biology. Vol. 458, 119-132.
• Lee C.W. and Park J.A. 2000. Assessment of HIV/AIDS-related health performance using an artificial neural network. Information & Management, vol. 38, 231-238
• Loannidis J.P.A. et. al. 1997. Use of Neural Networks to Model Complex Immunogenetic Associations of disease: Human Leukocyte Antigen Impact on the Progression of Human Immunodeficiency Virus Infection. American Journal of Epidemiology. vol. 147, Issue5. 464-471.
• NNelwamondo et. al. 2007. Missing data: A comparison of neural network and expectation maximization techniques. Current Science, vol. 93, No. 11.
• Pajares G. et. al. 2010. A Hopfield Neural Network for Combining Classifiers applied to Textured Images. Neural Networks. Vol. 23. Issue 1, 144-153.
• Pandey B. et. al. 2012. Evolutionary Modular Neural Network Approach for Breast Cancer Diagnosis. International Journal of Computer Science. Vol. 9, Issue 1, No. 2, 1694-0814.
• Pasomsub et. al. 2010. The Application of Artificial Neural Networks for Phenotypic Drug Resistance Prediction: Evaluation and Comparison with Other Interpretation Systems. Japanese journal of infectious diseases. Vol. 63, No. 2, 87-94.
• Pradhan M. and Sahu R.K. Multilayer perceptron network in HIV/AIDS application. International Journal of Computer Applications in Engineering Sciences (IJCAES), vol. I, Issue I.
• Purwanto et. al. 2011. Improved Adaptive Neuro-Fuzzy Inference System for HIV/AIDS Time Series Prediction. Communications in Computer and Information Science, vol. 253, Part 1, 1-13.
• Rebizant W. et. al. 2011. Fundamentals of System Analysis and Synthesis. Digital Signal Processing in Power System Protection and Control Signals and Communication Technology. 29-52.
• Resino S. et. al. 2011. An artificial neural network improves the non-invasive diagnosis of significant fibrosis in HIV/HCV coinfected patients. Journal of Infection. 62, 77e86.
• Reuter U. and Moller B. 2010. Artificial Neural Networks for Forecasting of Fuzzy Time Series. Computer-Aided Civil and Infrastructure Engineering. Vol. 25. Issue 5, 363-374.
• Singh Y. and Mars M. 2010. Support vector machines to forecast changes in CD4 count of HIV-1 positive