Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review
}
TY - CHAP
T1 - Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks
AU - Dondelinger, Frank
AU - Mukherjee, Sach
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Changepoint models
KW - Dynamic Bayesian networks
KW - Time-varying networks
KW - case report
KW - clinical article
KW - Drosophila melanogaster
KW - gene expression
KW - human
KW - life cycle
KW - nonhuman
KW - semantics
U2 - 10.1007/978-1-4939-8882-2_2
DO - 10.1007/978-1-4939-8882-2_2
M3 - Chapter (peer-reviewed)
SN - 9781493988815
T3 - Methods in Molecular Biology
SP - 25
EP - 48
BT - Gene Regulatory Networks
A2 - Sanguinetti, Guido
A2 - Anh Huynh-Thu, Vân
PB - Humana Press Inc.
CY - New York
ER -