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.
Table of Content
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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