00000cmm a22000004i 4500 UP-99796217611094778 Buklod 20140128111246.0 m go j cr |n |||auu|a 110429s2011 xxu u | eng d 9781441988195 (eBook) (iLib)UPD-00212404077 DLC DML eng DMLUC QA 402 H67 2011eb Horvath, Steve Weighted network analysis [electronic resource] applications in genomics and systems biology Steve Horvath. New York, N.Y. Springer c2011. 1 online resource (xxiii, 421 p.) ill. (some col.) 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. IP-based subscription, access limited to within on-campus computer network. Access via Electronic Resources of the UPD University Library Website. This book presents state-of-the-art methods, software and applications surrounding weighted networks. Most methods and results also apply to unweighted networks. Although aspects of weighted network analysis relate to standard data mining methods, the intuitive network language and analysis framework transcend any particular analysis method. Weighted networks give rise to data reduction methods, clustering procedures, visualization methods, data exploratory methods, and intuitive approaches for integrating disparate data sets. Weighted networks have been used to analyze a variety of high dimensional genomic data sets including gene expression-, epigenetic-, methylation-, proteomics-, and fMRI- data. Chapters explore the fascinating topological structure of weighted networks and provide geometric interpretations of network methods. Powerful systems-level analysis methods result from combining network- with data mining methods. The book not only describes the WGCNA R package but also other software packages. Weighted gene co-expression network applications, real data sets, 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 book 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.  Electronic reproduction. New York Springerlink 2011. Available via World Wide Web through SpringerLink. System analysis Data processing. Data mining. Life sciences. Human genetics. Bioinformatics. Biological models. Biology Data processing. Electronic books. SpringerLink (Online service). Available for the University of the Philippines Diliman via SpringerLink. Click here to access http://link.springer.com/book/10.1007/978-1-4419-8819-5 (viewed 12 May 2014) FO Monograph UPD DMLR Electronic Resource