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Large-scale inference of conjunctive Bayesian networks.

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Montazeri Hesam, Kuipers Jack, Kouyos Roger, Böni Jürg, Yerly Sabine, Klimkait Thomas, Aubert Vincent, Günthard Huldrych F, Beerenwinkel Niko, Beerenwinkel Niko,
Project Swiss HIV Cohort Study (SHCS)
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Original article (peer-reviewed)

Journal Bioinformatics (Oxford, England)
Volume (Issue) 32(17)
Page(s) 727 - 735
Title of proceedings Bioinformatics (Oxford, England)
DOI 10.1093/bioinformatics/btw459

Open Access

Type of Open Access Publisher (Gold Open Access)


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. The proposed method is implemented as an R package available at CONTACT: Supplementary data are available at Bioinformatics online.