Titre
Large-scale inference of conjunctive Bayesian networks.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Montazeri, H.
Auteure/Auteur
Kuipers, J.
Auteure/Auteur
Kouyos, R.
Auteure/Auteur
Böni, J.
Auteure/Auteur
Yerly, S.
Auteure/Auteur
Klimkait, T.
Auteure/Auteur
Aubert, V.
Auteure/Auteur
Günthard, H.F.
Auteure/Auteur
Beerenwinkel, N.
Auteure/Auteur
Groupes de travail
Swiss HIV Cohort Study
Liens vers les personnes
Liens vers les unités
ISSN
1367-4811
Statut éditorial
Publié
Date de publication
2016-09-01
Volume
32
Numéro
17
Première page
i727
Dernière page/numéro d’article
i735
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Publication Status: ppublish
Résumé
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 https://github.com/cbg-ethz/MC-CBN CONTACT: niko.beerenwinkel@bsse.ethz.ch
Supplementary data are available at Bioinformatics online.
The proposed method is implemented as an R package available at https://github.com/cbg-ethz/MC-CBN CONTACT: niko.beerenwinkel@bsse.ethz.ch
Supplementary data are available at Bioinformatics online.
PID Serval
serval:BIB_827A78409471
PMID
Open Access
Oui
Date de création
2016-09-16T19:36:19.008Z
Date de création dans IRIS
2025-05-20T20:58:17Z