Gene Regulatory Networks: Methods and Protocols

Guido Sanguinetti; Van Anh Huynh-Thu

doi:10.1007/978-1-4939-8882-2

Gene Regulatory Networks: Methods and Protocols

Guido Sanguinetti (Editor), Van Anh Huynh-Thu (Editor)

Research output: Book/Report › Book

Abstract

This volume explores recent techniques for the computational inference of gene regulatory networks (GRNs). The chapters in this book cover topics such as methods to infer GRNs from time-varying data; the extraction of causal information from biological data; GRN inference from multiple heterogeneous data sets; non-parametric and hybrid statistical methods; the joint inference of differential networks; and mechanistic models of gene regulation dynamics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, descriptions of recently developed methods for GRN inference, applications of these methods on real and/ or simulated biological data, and step-by-step tutorials on the usage of associated software tools.
Cutting-edge and thorough, Gene Regulatory Networks: Methods and Protocols is an essential tool for evaluating the current research needed to further address the common challenges faced by specialists in this field.

Original language	English
Publisher	Humana Press
Number of pages	285
ISBN (Electronic)	978-1-4939-8881-5
ISBN (Print)	978-1-4939-8881-5
DOIs	https://doi.org/10.1007/978-1-4939-8882-2
Publication status	Published - 2019

Publication series

Name	Methods in Molecular Biology Series Volume
Publisher	Humana Press
Volume	1883
ISSN (Print)	1064-3745

Access to Document

10.1007/978-1-4939-8882-2

https://link.springer.com/book/10.1007%2F978-1-4939-8882-2

2 Chapter

Gene Regulatory Network Inference: An Introductory Survey
Huynh-Thu, V. A. & Sanguinetti, G., 2019, Gene Regulatory Networks: Methods and Protocols. Sanguinetti, G. & Huynh-Thu, V. A. (eds.). New York, NY: Springer, p. 1-23 23 p.
Research output: Chapter in Book/Report/Conference proceeding › Chapter
Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3
Huynh-Thu, V. A. & Sanguinetti, G., 2019, Gene Regulatory Networks: Methods and Protocols. Sanguinetti, G. & Huynh-Thu, V. A. (eds.). New York, NY: Springer, p. 217-233 17 p.
Research output: Chapter in Book/Report/Conference proceeding › Chapter

Cite this

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title = "Gene Regulatory Networks: Methods and Protocols",

abstract = "This volume explores recent techniques for the computational inference of gene regulatory networks (GRNs). The chapters in this book cover topics such as methods to infer GRNs from time-varying data; the extraction of causal information from biological data; GRN inference from multiple heterogeneous data sets; non-parametric and hybrid statistical methods; the joint inference of differential networks; and mechanistic models of gene regulation dynamics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, descriptions of recently developed methods for GRN inference, applications of these methods on real and/ or simulated biological data, and step-by-step tutorials on the usage of associated software tools.Cutting-edge and thorough, Gene Regulatory Networks: Methods and Protocols is an essential tool for evaluating the current research needed to further address the common challenges faced by specialists in this field.",

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AB - This volume explores recent techniques for the computational inference of gene regulatory networks (GRNs). The chapters in this book cover topics such as methods to infer GRNs from time-varying data; the extraction of causal information from biological data; GRN inference from multiple heterogeneous data sets; non-parametric and hybrid statistical methods; the joint inference of differential networks; and mechanistic models of gene regulation dynamics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, descriptions of recently developed methods for GRN inference, applications of these methods on real and/ or simulated biological data, and step-by-step tutorials on the usage of associated software tools.Cutting-edge and thorough, Gene Regulatory Networks: Methods and Protocols is an essential tool for evaluating the current research needed to further address the common challenges faced by specialists in this field.

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ER -

Gene Regulatory Networks: Methods and Protocols

Abstract

Publication series

Access to Document

Fingerprint

Research output

Gene Regulatory Network Inference: An Introductory Survey

Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3

Cite this