Weighted network analysis: applications in genomics and systems biology

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. 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 biologicallyinteresting case studies that describe data analysis strategies and results. INDICE: 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. Geometricinterpretation 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 directednetworks. 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 basedon the joint probability distribution of random variables. Index.

• ISBN: 978-1-4419-8818-8
• Editorial: Springer New York
• Encuadernacion: Cartoné
• Páginas: 414
• Fecha Publicación: 29/05/2011
• Nº Volúmenes: 1
• Idioma: Inglés