Gene Regulatory Networks (inbunden)
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Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
430
Utgivningsdatum
2018-12-14
Upplaga
1st ed. 2019
Förlag
Humana Press Inc.
Medarbetare
Sanguinetti, Guido (ed.), Huynh-Thu, van Anh (ed.)
Illustratör/Fotograf
Bibliographie 39 schwarz-weiße und 68 farbige Abbildungen
Illustrationer
71 Illustrations, color; 43 Illustrations, black and white; XI, 430 p. 114 illus., 71 illus. in colo
Dimensioner
254 x 178 x 25 mm
Vikt
985 g
Antal komponenter
1
Komponenter
1 Hardback
ISBN
9781493988815
Gene Regulatory Networks (inbunden)

Gene Regulatory Networks

Methods and Protocols

Inbunden Engelska, 2018-12-14
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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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"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)

Innehållsförteckning

Preface...Table of Contents...Contributing Authors... 1. Gene Regulatory Network Inference: An Introductory SurveyVan Anh Huynh-Thu and Guido Sanguinetti 2. Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian NetworksFrank Dondelinger and Sach Mukherjee 3. Overview and Evaluation of Recent Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data Marco Grzegorczyk, Andrej Aderhold, and Dirk Husmeier 4. Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data Lingfei Wang and Tom Michoel 5. Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks Alex White and Matthieu Vignes 6. A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer Julien Chiquet, Guillem Rigaill, and Martina Sundqvist 7. Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks Alireza Fotuhi Siahpirani, Deborah Chasman, and Sushmita Roy 8. Unsupervised Gene Network Inference with Decision Trees and Random Forests Van Anh Huynh-Thu and Pierre Geurts 9. Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3 Van Anh Huynh-Thu and Guido Sanguinetti 10. Network Inference from Single-Cell Transcriptomic Data Helena Todorov, Robrecht Cannoodt, Wouter Saelens, and Yvan Saeys 11. Inferring Gene Regulatory Networks from Multiple Datasets Christopher A. Penfold, Iulia Gherman, Anastasiya Sybirna, and David L. Wild 12. Unsupervised GRN Ensemble Pau Bellot, Philippe Salembier, Ngoc C. Pham, and Patrick E. Meyer 13. Learning Differential Module Networks across Multiple Experimental Conditions Pau Erola, Eric Bonnet, and Tom Michoel 14. Stability in GRN Inference Giuseppe Jurman, Michele Filosi, Roberto Visintainer, Samantha Riccadonna, and Cesare Furlanello 15. Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling Olivia Angelin-Bonnet, Patrick J. Biggs, and Matthieu Vignes 16. Scalable Inference of Ordinary Differential Equation Models of Biochemical ProcessesFabian Froehlich, Carolin Loos, and Jan Hasenauer