About this book
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.
Bayesian networks Gaussian processes data simulation time series expression single-cell transcriptomic
Editors and affiliations
- DOI https://doi.org/10.1007/978-1-4939-8882-2
- Copyright Information Springer Science+Business Media, LLC, part of Springer Nature 2019
- Publisher Name Humana Press, New York, NY
- eBook Packages Springer Protocols
- Print ISBN 978-1-4939-8881-5
- Online ISBN 978-1-4939-8882-2
- Series Print ISSN 1064-3745
- Series Online ISSN 1940-6029
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