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

  • Frank DondelingerEmail author
  • Sach Mukherjee
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1883)

Abstract

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.

Key words

Time-varying networks Dynamic Bayesian networks Changepoint models 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Lancaster Medical SchoolLancaster UniversityLancasterUK
  2. 2.German Center for Neurodegenerative Diseases (DZNE)BonnGermany

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