Unveiling HIV mutational networks associated to pharmacological selective pressure: a temporal Bayesian approach
Pablo Hernández-Leal, Alma Rios-Flores, Felipe Orihuela‐Espina, Santiago Ávila‐Ríos, Gustavo Reyes‐Terán, Jesús A. González, Eduardo F. Morales, Luis Enrique Sucar · 2011
Much of the HIV (Human Immunodeficiency Virus) success is due to its evolving capabilities. Understanding viral evolution and its relation to pharmacology is of utmost importance in fighting diseases caused by the HIV. Although the mutations conferring drug resistance are mostly known, the dynamics of the appearance chain of those mutations remains poorly understood. Here we apply a Temporal Nodes Bayesian Network (TNBN) to data extracted from the HIV Stanford database to explore the probabilistic relationships between mutations and antiretrovirals. We aim to unveil existing mutation networks and establish their probabilistic formation sequence. The model predictive accuracy is evaluated in terms of relative Brier score, relative time error and total number of intervals. Robustness of the model is hinted by consistency between two model instances. The learned models capture known relationships, qualitatively providing some measure of validity. Finally, two previously unseen mutational networks are retrieved and their probabilistic temporal sequentiation uncovered. We demonstrate the usefulness of TNBN for studying drug-mutation and mutation-mutation networks and expect to impact the combat against HIV infection by facilitating better treatment planning.