Contrasting Temporal Bayesian Network Models for Analyzing HIV Mutations

Evolution is an important aspect of viral diseases such as influenza, hepatitis and the human immunodeficiency virus (HIV). This evolution impacts the development of successful vaccines and antiviral drugs, as mutations increase drug resistance. Although mutations providing drug resistance are mostly known, the dynamics of the occurrence of those mutations remains poorly understood. A common graphical model to handle temporal information are Dynamic Bayesian Networks. However, other options to address this problem exist. This is the case of Temporal Nodes Bayesian Networks. In this paper we used both approaches for modeling the relationships between antiretroviral drugs and HIV mutations, in order to analyze temporal occurrence of specific mutations in HIV that may lead to drug resistance. We compare the strengths and limitations of each of these two temporal approaches for this particular problem and show that the obtained models were able to capture some mutational pathways already known (obtained by clinical experimentation).