Gene Regulatory Networks : Methods and Protocols / edited by Guido Sanguinetti, Vân Anh Huynh-Thu.

Format:

Book

Contributor:

Sanguinetti, Guido, 1974- editor.

Huynh-Thu, Vân Anh, editor.

SpringerLink (Online service)

Series:

Springer Protocols (Springer-12345)

Methods in molecular biology 1064-3745 ; 1883.

Methods in Molecular Biology, 1064-3745 ; 1883

Language:

English

Subjects (All):

Biotechnology.

Local Subjects:

Biotechnology.

Physical Description:

1 online resource (XI, 430 pages) : 114 illustrations, 71 illustrations in color.

Contained In:

Springer eBooks

Place of Publication:

New York, NY : Springer New York : Imprint: Humana Press, 2019.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

9781493988822

Access Restriction:

Restricted for use by site license.