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Large-scale inference of conjunctive Bayesian networks

Montazeri, Hesam
Kuipers, Jack
Kouyos, Roger
Böni, Jürg
Klimkait, Thomas
Günthard, Huldrych F
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Published in Bioinformatics. 2016, vol. 32, no. 17, p. i727-i735
Abstract The continuous time conjunctive Bayesian network (CT-CBN) is a graphical model for analyzing the waiting time process of the accumulation of genetic changes (mutations). CT-CBN models have been successfully used in several biological applications such as HIV drug resistance development and genetic progression of cancer. However, current approaches for parameter estimation and network structure learning of CBNs can only deal with a small number of mutations (<20). Here, we address this limitation by presenting an efficient and accurate approximate inference algorithm using a Monte Carlo expectation-maximization algorithm based on importance sampling. The new method can now be used for a large number of mutations, up to one thousand, an increase by two orders of magnitude. In simulation studies, we present the accuracy as well as the running time efficiency of the new inference method and compare it with a MLE method, expectation-maximization, and discrete time CBN model, i.e. a first-order approximation of the CT-CBN model. We also study the application of the new model on HIV drug resistance datasets for the combination therapy with zidovudine plus lamivudine (AZT + 3TC) as well as under no treatment, both extracted from the Swiss HIV Cohort Study database.
Keywords AlgorithmsBayes TheoremCohort StudiesHumansMonte Carlo MethodMutation
PMID: 27587695
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Research group Groupe Laurent Kaiser (virologie clinique) (668)
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MONTAZERI, Hesam et al. Large-scale inference of conjunctive Bayesian networks. In: Bioinformatics, 2016, vol. 32, n° 17, p. i727-i735. doi: 10.1093/bioinformatics/btw459 https://archive-ouverte.unige.ch/unige:98819

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Deposited on : 2017-11-09

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