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Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)peer-review

  • Frank Dondelinger
  • Sach Mukherjee
Publication date2019
Host publicationGene Regulatory Networks
EditorsGuido Sanguinetti, Vân Anh Huynh-Thu
Place of PublicationNew York
PublisherHumana Press Inc.
Number of pages24
ISBN (Electronic)9781493988822
ISBN (Print)9781493988815
<mark>Original language</mark>English

Publication series

NameMethods in Molecular Biology
PublisherHumana Press
ISSN (Print)1064-3745


In this chapter, we review the problem of network inference from time-course data, focusing on a class of graphical models known as dynamic Bayesian networks (DBNs). We discuss the relationship of DBNs to models based on ordinary differential equations, and consider extensions to nonlinear time dynamics. We provide an introduction to time-varying DBN models, which allow for changes to the network structure and parameters over time. We also discuss causal perspectives on network inference, including issues around model semantics that can arise due to missing variables. We present a case study of applying time-varying DBNs to gene expression measurements over the life cycle of Drosophila melanogaster. We finish with a discussion of future perspectives, including possible applications of time-varying network inference to single-cell gene expression data. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.