Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Objective: The human immunodeficiency virus (HIV)is one ofthe 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) modelto data extracted from
the Stanford HIVdrug 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 mutational
pathways in immune selection with relevance for antiretroviral drug development and therapy
plan.
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) modelto data extracted from
the Stanford HIVdrug 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 mutational
pathways in immune selection with relevance for antiretroviral drug development and therapy
plan.
Date Issued
2013-04-03
Date Acceptance
2013-01-18
Citation
Artificial Intelligence in Medicine, 2013, 57 (3), pp.185-195
ISSN
0933-3657
Publisher
Elsevier
Start Page
185
End Page
195
Journal / Book Title
Artificial Intelligence in Medicine
Volume
57
Issue
3
Copyright Statement
© 2012 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Subjects
Medical Informatics
08 Information And Computing Sciences
09 Engineering
Publication Status
Published