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Gene Regulatory Networks

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Cover of 'Gene Regulatory Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Gene Regulatory Network Inference: An Introductory Survey
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    Chapter 2 Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks
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    Chapter 3 Overview and Evaluation of Recent Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data
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    Chapter 4 Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data
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    Chapter 5 Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks
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    Chapter 6 A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer
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    Chapter 7 Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks
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    Chapter 8 Unsupervised Gene Network Inference with Decision Trees and Random Forests
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    Chapter 9 Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3
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    Chapter 10 Network Inference from Single-Cell Transcriptomic Data
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    Chapter 11 Inferring Gene Regulatory Networks from Multiple Datasets
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    Chapter 12 Unsupervised GRN Ensemble
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    Chapter 13 Learning Differential Module Networks Across Multiple Experimental Conditions
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    Chapter 14 Stability in GRN Inference
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    Chapter 15 Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling
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    Chapter 16 Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes
Attention for Chapter 6: A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer
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Chapter title
A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer
Chapter number 6
Book title
Gene Regulatory Networks
Published by
Humana Press, New York, NY, December 2018
DOI 10.1007/978-1-4939-8882-2_6
Pubmed ID
Book ISBNs
978-1-4939-8881-5, 978-1-4939-8882-2
Authors

Julien Chiquet, Guillem Rigaill, Martina Sundqvist, Chiquet, Julien, Rigaill, Guillem, Sundqvist, Martina

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 29%
Student > Doctoral Student 2 14%
Student > Bachelor 2 14%
Professor 2 14%
Researcher 1 7%
Other 2 14%
Unknown 1 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 29%
Mathematics 3 21%
Agricultural and Biological Sciences 3 21%
Medicine and Dentistry 1 7%
Neuroscience 1 7%
Other 1 7%
Unknown 1 7%