Steve Horvath

Weighted Network Analysis

Applications in Genomics and Systems Biology

• Gebundenes Buch

Produktbeschreibung

Weighted Correlation Network Analysis and Systems Biologic Applications presents state-of-the-art methods, software and applications surrounding weighted networks. Most methods also apply also to unweighted networks. The book reviews data mining methods and analysis strategies. Applications and exercises guide the reader on how to use these methods in practice, e.g. in systems-biologic or systems-genetic applications.The material is self-contained and only requires a minimum knowledge of statistics. The accessible material is intended for students, faculty, and data analysts in many fields including bioinformatics, computational biology, statistics, computer science, biology, genetics, applied mathematics, physics, and social science. Networks have been applied to analyze a variety of high dimensional data including gene expression-, epigenetic-, methylation-, RNA-seq, proteomics-, and fMRI- data.

Produktdetails

• Verlag: Springer / Springer New York / Springer, Berlin
• Artikelnr. des Verlages: 12542888, 978-1-4419-8818-8
• 2011
• Seitenzahl: 448
• Erscheinungstermin: 4. Mai 2011
• Englisch
• Abmessung: 241mm x 160mm x 30mm
• Gewicht: 806g
• ISBN-13: 9781441988188
• ISBN-10: 1441988181
• Artikelnr.: 32675942

Inhaltsangabe

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.