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Gene Regulatory Networks

Overview of attention for book
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 12: Unsupervised GRN Ensemble
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Citations

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Chapter title
Unsupervised GRN Ensemble
Chapter number 12
Book title
Gene Regulatory Networks
Published by
Humana Press, New York, NY, December 2018
DOI 10.1007/978-1-4939-8882-2_12
Pubmed ID
30547405
Book ISBNs
978-1-4939-8881-5, 978-1-4939-8882-2
Authors

Pau Bellot, Philippe Salembier, Ngoc C. Pham, Patrick E. Meyer, Bellot, Pau, Salembier, Philippe, Pham, Ngoc C., Meyer, Patrick E.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Professor 2 29%
Student > Bachelor 1 14%
Student > Doctoral Student 1 14%
Student > Ph. D. Student 1 14%
Researcher 1 14%
Other 0 0%
Unknown 1 14%
Readers by discipline Count As %
Computer Science 3 43%
Biochemistry, Genetics and Molecular Biology 2 29%
Medicine and Dentistry 1 14%
Unknown 1 14%

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