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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