Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks

dc.creatorPablo Francisco Hernández Leal
dc.creatorAlma Rios Flores
dc.creatorSANTIAGO AVILA RIOS
dc.creatorGUSTAVO REYES TERAN
dc.creatorJesús Antonio González Bernal
dc.creatorLinsey Fiedler Cameras
dc.creatorFelipe Orihuela Espina
dc.creatorEduardo Francisco Morales Manzanares
dc.creatorLuis Enrique Sucar Succar
dc.date2013
dc.date.accessioned2023-07-25T16:25:26Z
dc.date.available2023-07-25T16:25:26Z
dc.identifierhttp://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2349
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7807525
dc.descriptionObjective: 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 and material: 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%. Conclusion: 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 muta- tional pathways in immune selection with relevance for antiretroviral drug development and therapy plan.
dc.formatapplication/pdf
dc.languageeng
dc.publisherElsevier B.V.
dc.relationcitation:Hernández, P., et al., (2013). Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks, Artificial Intelligence in Medicine Vol. 2013 (57):185–195
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectinfo:eu-repo/classification/Probabilistic graphical models/Probabilistic graphical models
dc.subjectinfo:eu-repo/classification/Probabilistic learning/Probabilistic learning
dc.subjectinfo:eu-repo/classification/Human immunodeficiency virus mutations/Human immunodeficiency virus mutations
dc.subjectinfo:eu-repo/classification/cti/1
dc.subjectinfo:eu-repo/classification/cti/12
dc.subjectinfo:eu-repo/classification/cti/1203
dc.subjectinfo:eu-repo/classification/cti/1203
dc.titleDiscovering human immunodeficiency virus mutational pathways using temporal Bayesian networks
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.audiencestudents
dc.audienceresearchers
dc.audiencegeneralPublic


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