title: Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks.
abstract: OBJECTIVE
  The human immunodeficiency virus (HIV) is one of the fastest evolving organisms in the planet. Its remarkable variation capability makes HIV able to escape from multiple evolutionary forces naturally or artificially acting on it, through the development and selection of adaptive mutations. Although most drug resistance mutations have been well identified, the dynamics and temporal patterns of appearance of these mutations can still be further explored. The use of models to predict mutational pathways as well as temporal patterns of appearance of adaptive mutations could greatly benefit clinical management of individuals under antiretroviral therapy.
  
  
  METHODS
  We apply a temporal nodes Bayesian network (TNBN) model to data extracted from the Stanford HIV drug resistance database in order to explore the probabilistic relationships between drug resistance mutations and antiretroviral drugs unveiling possible mutational pathways and establishing their probabilistic-temporal sequence of appearance.
  
  
  RESULTS
  In a first experiment, we compared the TNBN approach with other models such as static Bayesian networks, dynamic Bayesian networks and association rules. TNBN achieved a 64.2% sparser structure over the static network. In a second experiment, the TNBN model was applied to a dataset associating antiretroviral drugs with mutations developed under different antiretroviral regimes. The learned models captured previously described mutational pathways and associations between antiretroviral drugs and drug resistance mutations. Predictive accuracy reached 90.5%.
  
  
  CONCLUSIONS
  Our results suggest possible applications of TNBN for studying drug-mutation and mutation-mutation networks in the context of antiretroviral therapy, with direct impact on the clinical management of patients under antiretroviral therapy. This opens new horizons for predicting HIV mutational pathways in immune selection with relevance for antiretroviral drug development and therapy plan.
authors.affiliation: Coordinación de Ciencias Computacionales, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro #1, Sta. María Tonantzintla, Puebla, Mexico. pablohl@ccc.inaoep.mx
authors.forename: Pablo
authors.initials: P
authors.lastname: Hernandez-Leal
authors.forename: Alma
authors.initials: A
authors.lastname: Rios-Flores
authors.forename: Santiago
authors.initials: S
authors.lastname: Avila-Rios
authors.forename: Gustavo
authors.initials: G
authors.lastname: Reyes-Terán
authors.forename: Jesus A
authors.initials: JA
authors.lastname: Gonzalez
authors.forename: Lindsey
authors.initials: L
authors.lastname: Fiedler-Cameras
authors.forename: Felipe
authors.initials: F
authors.lastname: Orihuela-Espina
authors.forename: Eduardo F
authors.initials: EF
authors.lastname: Morales
authors.forename: L Enrique
authors.initials: LE
authors.lastname: Sucar
mesh_terms: D019380:Anti-HIV Agents / Q000494:pharmacology; D001499:Bayes Theorem; D006678:HIV / Q000187:drug effects / Q000235:genetics* / Q000502:physiology; D009154:Mutation; D014779:Virus Replication
chemical_list: D019380:Anti-HIV Agents