Gene Regulatory Networks (eBook, PDF)

Methods and Protocols
Redaktion: Sanguinetti, Guido; Huynh-Thu, Vân Anh

• Format: PDF

Produktbeschreibung

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 commonchallenges faced by specialists in this field.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 430
• Erscheinungstermin: 13. Dezember 2018
• Englisch
• ISBN-13: 9781493988822
• Artikelnr.: 64700288

Inhaltsangabe

Gene Regulatory Network Inference: An Introductory Survey.- Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks.- Overview and Evaluation of Recent Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data.- Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data.- Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks.- A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer.- Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks.- Unsupervised Gene Network Inference with Decision Trees and Random Forests.- Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3.- Network Inference from Single-Cell Transcriptomic Data.- Inferring Gene Regulatory Networks from Multiple Datasets.- Unsupervised GRN Ensemble.- Learning Differential Module Networks across Multiple Experimental Conditions.- Stability in GRN Inference.- Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling.- Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes.

Rezensionen

"The book covers a variety of computational and mathematical aspects related to the inference of gene regulatory networks. The style of the chapters seamlessly binds the comprehensive overview that recommended the book to junior researchers and the thorough description of topics, highlighting new direction of research, that would appeal to post-graduates and established researchers." (Irina Ioana Mohorianu, zbMATH 1417.92005, 2019)