Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Dynamic network models for forecasting
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Learning multiple evolutionary pathways from cross-sectional data
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Learning Bayesian Networks
COMPARISON OF TWO TYPES OF EVENT BAYESIAN NETWORKS: A CASE STUDY
Applied Artificial Intelligence
Temporal Data Mining of HIV Registries: Results from a 25 Years Follow-Up
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks
Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
A temporal Bayesian network for diagnosis and prediction
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Editorial: Bayesian networks in biomedicine and health-care
Artificial Intelligence in Medicine
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
Guest editorial: Probabilistic problem solving in biomedicine
Artificial Intelligence in Medicine
Describing disease processes using a probabilistic logic of qualitative time
Artificial Intelligence in Medicine
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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 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 mutational pathways in immune selection with relevance for antiretroviral drug development and therapy plan.