Several antiretroviral drugs have been approved for use in HIV infected people. Despite
efforts made by the scientific community, an effective drug that kills the virus has not been developed
yet. A lot of computational algorithms have been used for finding mutations associated with drug
resistance as well as for prediction of HIV resistance. This article provides an overview of machine
learning techniques used to predict the HIV drug resistance. The different types of studies done will be
reviewed through the following characteristics: different representations of the problem, ARVs,
methods to reduce dimensionality and algorithms of machine learning used.
Keywords: HIV prediction, machine learning, feature selection, feature extraction, drug resistance.
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