Gene Regulatory Networks: Methods and Protocols

ISBN-13: 9781493988815 / Angielski / Twarda / 2018 / 430 str.

Guido Sanguinetti; Van Anh Huynh-Thu

Recenzje

"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)

Spis treści

Preface…

Table of Contents…

Contributing Authors…

1. Gene Regulatory Network Inference: An Introductory Survey

Vân Anh Huynh-Thu and Guido Sanguinetti 

2. Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks

Frank 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 

Vân Anh Huynh-Thu and Pierre Geurts

9. Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3 

Vân 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 Processes

Fabian Fröhlich, Carolin Loos, and Jan Hasenauer

Blurb

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.