Large-scale inference of conjunctive Bayesian networks.
- Montazeri H Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
- Kuipers J Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
- Kouyos R Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland Institute of Medical Virology.
- Böni J Swiss National Center for Retroviruses, Institute of Medical Virology, University of Zurich, Zurich 8057, Switzerland.
- Yerly S Laboratory of Virology, Division of Infectious Diseases, Geneva University Hospital, Geneva, Switzerland.
- Klimkait T Department of Biomedicine-Petersplatz, University of Basel, Basel, Switzerland.
- Aubert V Division of Immunology and Allergy, University Hospital Lausanne, Lausanne, Switzerland.
- Günthard HF Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland Institute of Medical Virology.
- Beerenwinkel N Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
- 2016-09-03
Published in:
- Bioinformatics (Oxford, England). - 2016
English
UNLABELLED
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.
AVAILABILITY AND IMPLEMENTATION
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 INFORMATION
Supplementary data are available at Bioinformatics online.
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.
AVAILABILITY AND IMPLEMENTATION
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 INFORMATION
Supplementary data are available at Bioinformatics online.
- Language
-
- English
- Open access status
- hybrid
- Identifiers
-
- DOI 10.1093/bioinformatics/btw459
- PMID 27587695
- Persistent URL
- https://sonar.rero.ch/global/documents/155243
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