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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 4: Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data
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Chapter title
Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data
Chapter number 4
Book title
Gene Regulatory Networks
Published by
Humana Press, New York, NY, December 2018
DOI 10.1007/978-1-4939-8882-2_4
Pubmed ID
Book ISBNs
978-1-4939-8881-5, 978-1-4939-8882-2

Lingfei Wang, Tom Michoel, Wang, Lingfei, Michoel, Tom

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Unspecified 1 17%
Professor 1 17%
Researcher 1 17%
Student > Postgraduate 1 17%
Other 0 0%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 33%
Agricultural and Biological Sciences 2 33%
Unspecified 1 17%
Immunology and Microbiology 1 17%