Gene Regulatory Networks, 1st ed. 2019 Methods and Protocols Methods in Molecular Biology Series, Vol. 1883

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 addressthe common challenges faced by specialists in this field.

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.