Proceeding talk – Theme: Systems.
Abstract
The continuous time conjunctive Bayesian network (CT-CBN) is a graphical model for analyzing the waiting time process of the accumulation of mutations. CT-CBN models have been successfully used in HIV drug resistance development and genetic progression of cancer. However, current approaches for inference of CBNs can only deal with a small number of mutations. Here, we address this limitation by presenting an efficient approximate inference algorithm using a Monte Carlo expectation-maximization algorithm. This method can 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 and running time efficiency of the new method and compare it with alternative methods. We also study its application on HIV drug resistance datasets for the combination therapy with zidovudine plus lamivudine as well as under no treatment, both extracted from the Swiss HIV Cohort Study database.
Authors
Hesam Montazeri, ETH Zurich, Switzerland
Jack Kuipers, ETH Zurich, D-BSSE, Computational Biology Group, Switzerland
Roger Kouyos, Institute of Medical Virology, University of Zurich, Zurich 8057, Switzerland
Jürg Böni, Swiss National Center for Retroviruses, Institute of Medical Virology, University of Zurich, Zurich, Switzerland
Sabin Yerly, Laboratory of Virology, Division of Infectious Diseases, Geneva University Hospital, Geneva, Switzerland
Thomas Klimkait, University of Basel, Switzerland
Vincent Aubert, Division of Immunology and Allergy, University Hospital Lausanne, Lausanne, Switzerland
Huldrych Günthard, University Hospital Zurich, Division of Infectious Diseases and Hospital Epidemiology, Switzerland
Niko Beerenwinkel, ETH Zurich, Switzerland