000 04004nam a22004935i 4500
001 978-1-4419-8819-5
003 DE-He213
005 20140220083728.0
007 cr nn 008mamaa
008 110429s2011 xxu| s |||| 0|eng d
020 _a9781441988195
_9978-1-4419-8819-5
024 7 _a10.1007/978-1-4419-8819-5
_2doi
050 4 _aQH301-705
072 7 _aPSA
_2bicssc
072 7 _aSCI086000
_2bisacsh
072 7 _aSCI064000
_2bisacsh
082 0 4 _a570
_223
100 1 _aHorvath, Steve.
_eauthor.
245 1 0 _aWeighted Network Analysis
_h[electronic resource] :
_bApplications in Genomics and Systems Biology /
_cby Steve Horvath.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2011.
300 _aXXIII, 421p. 54 illus., 46 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPreface -- 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.
520 _aThis 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. 
650 0 _aLife sciences.
650 0 _aHuman genetics.
650 0 _aBioinformatics.
650 0 _aBiological models.
650 0 _aBiology
_xData processing.
650 1 4 _aLife Sciences.
650 2 4 _aSystems Biology.
650 2 4 _aBioinformatics.
650 2 4 _aHuman Genetics.
650 2 4 _aComputer Appl. in Life Sciences.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441988188
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-8819-5
912 _aZDB-2-SBL
999 _c106011
_d106011