Machine learning approaches to study HIV/AIDS infection: A Review

Abstract

In this review, PubMed database has been explored to elucidate the problems related to HIV/AIDS, which have been solved previously using various machine learning approaches and some other techniques. Literatures from the epidemic years of HIV/AIDS till February, 2017 have been examined and problems such as prediction of HIV/AIDS protease cleavage sites and inhibitors, prediction of coreceptors usage for viral entry, development of anti-viral agents and prediction of response, resistance and adverse effect of antiretroviral therapy have been considered for the current study. Complications associated with HIV/AIDS infection as well as all three stages of HIV infection have been described. HIV virus binding to the coreceptors CCR5 and CXCR4 are delineated to show the signifi cant role of the coreceptors for the anti-HIV drug development. After exploring various datasets, viral tropisms are found to be relevant to the viral third V3 region of the HIV virus binding.