Gene regulatory networks. Methods and protocols
type: Item
instance of: scholarly article
title: Gene regulatory networks. Methods and protocols
zbMATH Open document ID: 1417.92005
DOI: 10.1007/978-1-4939-8882-2
published in: Methods in Molecular Biology
publication date: 2019-02-13T00:00:00Z
review text: This book comprises of sixteen chapters that tackle different angles and methods proposed for the inference and characterization of regulatory networks. This heterogeneous collection of approaches is a timely addition to a fast progressing field and presents itself both as a comprehensive introductory overview of the field and as a stepping stone for future advances. The book commences with an overarching overview of the computational aspects related to the inference of gene regulatory networks provided by the editors. The introduction of the biological problem and its dependencies with the high throughput approaches, currently widely available and intensively used, are followed by the mathematical formulation and the description of few data driven, probabilistic and dynamical approaches. Evaluation methods and a list of tools are also included. The next two chapters focus on time series; in the second one, the authors present a method for inferring networks based on statistical inference using dynamic Bayesian networks (DBNs); extensions to nonlinear time dynamics and the consequences of missing variable are also discussed. In the third chapter, the authors give a broad overview of the state-of-the-art, recent methods for inferring networks from time series data, providing guidance and practical pointers for publicly available software. The next four chapters discuss different sides of causal networks, applied at genomic scale. In the fourth chapter, the authors use whole transcriptomic datasets (genomic and transcriptomic data) to infer such networks; the examples are built on 3000 genes from the Geuvadis dataset using Findr. The causal analysis is continued in the fifth chapter in which the authors present an empirical study in small networks, using an underlying Bayesian approach. The next chapter uses a breast cancer setup to illustrate a multi-attribute Gaussian graphical model (GGM) for inferring multiscale networks; a group-Lasso approach is used for linking…
reviewed by: Irina Ioana Mohorianu
Mathematics Subject Classification ID: 92-06
Mathematics Subject Classification ID: 92C42
Mathematics Subject Classification ID: 92C40
Mathematics Subject Classification ID: 92-08
zbMATH DE Number: 7021633
zbMATH Keywords: statistical network inference
zbMATH Keywords: gene regulatory network
zbMATH Keywords: timeseries
zbMATH Keywords: observational data
zbMATH Keywords: Bayesian network
zbMATH Keywords: small network
zbMATH Keywords: integrative analysis
zbMATH Keywords: genome scale
zbMATH Keywords: decision tree
zbMATH Keywords: random forest
zbMATH Keywords: single-cell data
zbMATH Keywords: ensemble methods
zbMATH Keywords: differential module networks
zbMATH Keywords: stability inference
zbMATH Keywords: ordinary differential equations
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Gene regulatory networks. Methods and protocols (MaRDI profile type)
MaRDI profile type: Publication

Gene regulatory networks. Methods and protocols (full work available at URL)
full work available at URL: https://doi.org/10.1007/978-1-4939-8882-2

Gene regulatory networks. Methods and protocols (OpenAlex ID)
OpenAlex ID: W2913354234