000			04106nam a22005175i 4500
001			978-0-387-77240-0
003			DE-He213
005			20251006084412.0
007			cr nn 008mamaa
008			100608s2008 xxu| s |||| 0|eng d
020			_a9780387772400
020			_a99780387772400
024	7		_a10.1007/978-0-387-77240-0 _2doi
082	0	4	_a519.5 _223
100	1		_aHahne, Florian. _eauthor.
245	1	0	_aBioconductor Case Studies _h[electronic resource] / _cby Florian Hahne, Wolfgang Huber, Robert Gentleman, Seth Falcon.
250			_a1.
264		1	_aNew York, NY : _bSpringer New York, _c2008.
300			_bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aUse R!
505	0		_aThe ALL Dataset -- R and Bioconductor Introduction -- Processing Affymetrix Expression Data -- Two Color Arrays -- Fold Changes, Log Ratios, Background Correction, Shrinkage Estimation, and Variance Stabilization -- Easy Differential Expression -- Differential Expression -- Annotation and Metadata -- Supervised Machine Learning -- Unsupervised Machine Learning -- Using Graphs for Interactome Data -- Graph Layout -- Gene Set Enrichment Analysis -- Hypergeometric Testing Used for Gene Set Enrichment Analysis -- Solutions to Exercises.
520			_aBioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include * import and preprocessing of data from various sources * statistical modeling of differential gene expression * biological metadata * application of graphs and graph rendering * machine learning for clustering and classification problems * gene set enrichment analysis Each chapter of this book describes an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executed on a local computer, and readers are able to reproduce every computation, figure, and table. The authors of this book have longtime experience in teaching introductory and advanced courses to the application of Bioconductor software. Florian Hahne is a Postdoc at the Fred Hutchinson Cancer Research Center in Seattle, developing novel methodologies for the analysis of high-throughput cell-biological data. Wolfgang Huber is a research group leader in the European Molecular Biology Laboratory at the European Bioinformatics Institute in Cambridge. He has wide-ranging experience in the development of methods for the analysis of functional genomics experiments. Robert Gentleman is Head of the Program in Computational Biology at the Fred Hutchinson Cancer Research Center in Seattle, and he is one of the two authors of the original R system. Seth Falcon is a member of the R core team and former project manager and developer for the Bioconductor project.
650		0	_aSTATISTICS.
650		0	_aBIOINFORMATICS.
650		0	_aBIOLOGY _xMATHEMATICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICS FOR LIFE SCIENCES, MEDICINE, HEALTH SCIENCES.
650	2	4	_aCOMPUTATIONAL BIOLOGY/BIOINFORMATICS.
650	2	4	_aBIOINFORMATICS.
650	2	4	_aMATHEMATICAL BIOLOGY IN GENERAL.
700	1		_aHuber, Wolfgang. _eauthor.
700	1		_aGentleman, Robert. _eauthor.
700	1		_aFalcon, Seth. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387772394
830		0	_aUse R!
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-77240-0 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c58937 _d58937