Weighted Network Analysis: Applications in Genomics and Systems Biology

Author:   Steve Horvath
Publisher:   Springer-Verlag New York Inc.
Edition:   2011 ed.
ISBN:  

9781441988188


Pages:   421
Publication Date:   04 May 2011
Format:   Hardback
Availability:   In Print   Availability explained
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Weighted Network Analysis: Applications in Genomics and Systems Biology


Overview

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.

Full Product Details

Author:   Steve Horvath
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2011 ed.
Dimensions:   Width: 15.50cm , Height: 3.00cm , Length: 23.50cm
Weight:   0.834kg
ISBN:  

9781441988188


ISBN 10:   1441988181
Pages:   421
Publication Date:   04 May 2011
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Preface.- Networks and fundamental concepts.- Approximately factorizable networks.- Different type of network concepts.- Adjacency functions and their topological effects.- Correlation and gene co-expression networks.- Geometric interpretation of correlation networks using the singular value decomposition.- Constructing networks from matrices.- Clustering Procedures and module detection.- Evaluating whether a module is preserved in another network.- Association and statistical significance measures.- Structural equation models and directed networks.- Integrated weighted correlation network analysis of mouse liver gene expression data.- Networks based on regression models and prediction methods.- Networks between categorical or discretized numeric variables.- Networks based on the joint probability distribution of random variables.- Index.

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