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

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Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks. / Dondelinger, Frank; Mukherjee, Sach.

Gene Regulatory Networks. ed. / Guido Sanguinetti; Vân Anh Huynh-Thu. New York : Humana Press Inc., 2019. p. 25-48 (Methods in Molecular Biology; Vol. 1883).

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

Harvard

Dondelinger, F & Mukherjee, S 2019, Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks. in G Sanguinetti & V Anh Huynh-Thu (eds), Gene Regulatory Networks. Methods in Molecular Biology, vol. 1883, Humana Press Inc., New York, pp. 25-48. https://doi.org/10.1007/978-1-4939-8882-2_2

APA

Dondelinger, F., & Mukherjee, S. (2019). Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks. In G. Sanguinetti, & V. Anh Huynh-Thu (Eds.), Gene Regulatory Networks (pp. 25-48). (Methods in Molecular Biology; Vol. 1883). Humana Press Inc.. https://doi.org/10.1007/978-1-4939-8882-2_2

Vancouver

Dondelinger F, Mukherjee S. Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks. In Sanguinetti G, Anh Huynh-Thu V, editors, Gene Regulatory Networks. New York: Humana Press Inc. 2019. p. 25-48. (Methods in Molecular Biology). https://doi.org/10.1007/978-1-4939-8882-2_2

Author

Dondelinger, Frank ; Mukherjee, Sach. / Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks. Gene Regulatory Networks. editor / Guido Sanguinetti ; Vân Anh Huynh-Thu. New York : Humana Press Inc., 2019. pp. 25-48 (Methods in Molecular Biology).

Bibtex

@inbook{c060b770843247668f28f4ebd1907cee,
title = "Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks",
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. {\textcopyright} 2019, Springer Science+Business Media, LLC, part of Springer Nature.",
keywords = "Changepoint models, Dynamic Bayesian networks, Time-varying networks, case report, clinical article, Drosophila melanogaster, gene expression, human, life cycle, nonhuman, semantics",
author = "Frank Dondelinger and Sach Mukherjee",
year = "2019",
doi = "10.1007/978-1-4939-8882-2_2",
language = "English",
isbn = "9781493988815",
series = "Methods in Molecular Biology",
publisher = "Humana Press Inc.",
pages = "25--48",
editor = "Guido Sanguinetti and {Anh Huynh-Thu}, {V{\^a}n }",
booktitle = "Gene Regulatory Networks",

}

RIS

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 -