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Weighted Network Analysis

Applications in Genomics and Systems Biology

  • Book
  • © 2011

Overview

Authors:
  1. Steve Horvath

    1. University of California, Los Angeles, Los Angeles, USA

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  • This books describes the theory, application, and software of weighted gene co-expression network analysis

  • Serves as an introductory and comprehensive text on gene co-expression network methodology

  • The book includes biologically interesting case studies that describe data analysis strategies and results

  • Includes supplementary material: sn.pub/extras

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Table of contents (15 chapters)

  1. Front Matter

    Pages i-xxiii
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  2. Networks and Fundamental Concepts

    • Steve Horvath
    Pages 1-34

  3. Approximately Factorizable Networks

    • Steve Horvath
    Pages 35-43

  4. Different Types of Network Concepts

    • Steve Horvath
    Pages 45-75

  5. Adjacency Functions and Their Topological Effects

    • Steve Horvath
    Pages 77-89

  6. Correlation and Gene Co-Expression Networks

    • Steve Horvath
    Pages 91-121

  7. Geometric Interpretation of Correlation Networks Using the Singular Value Decomposition

    • Steve Horvath
    Pages 123-160

  8. Constructing Networks from Matrices

    • Steve Horvath
    Pages 161-178

  9. Clustering Procedures and Module Detection

    • Steve Horvath
    Pages 179-206

  10. Evaluating Whether a Module is Preserved in Another Network

    • Steve Horvath
    Pages 207-247

  11. Association Measures and Statistical Significance Measures

    • Steve Horvath
    Pages 249-277

  12. Structural Equation Models and Directed Networks

    • Steve Horvath
    Pages 279-320

  13. Integrated Weighted Correlation Network Analysis of Mouse Liver Gene Expression Data

    • Steve Horvath
    Pages 321-351

  14. Networks Based on Regression Models and Prediction Methods

    • Steve Horvath
    Pages 353-372

  15. Networks Between Categorical or Discretized Numeric Variables

    • Steve Horvath
    Pages 373-400

  16. Network Based on the Joint Probability Distribution of Random Variables

    • Steve Horvath
    Pages 401-411

  17. Back Matter

    Pages 413-421
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Keywords

About this book

High-throughput measurements of gene expression and genetic marker data facilitate systems biologic and systems genetic data analysis strategies. Gene co-expression networks have been used to study a variety of biological systems, bridging the gap from individual genes to biologically or clinically important emergent phenotypes.

Authors and Affiliations

  • University of California, Los Angeles, Los Angeles, USA

    Steve Horvath

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