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008			151030s2016 xxu| s |||| 0|eng d
020			_a9781493931064
024	7		_a10.1007/978-1-4939-3106-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
245	1	0	_aStatistical Analysis in Proteomics _cedited by Klaus Jung.
250			_a1st edition 2016
264		1	_aNew York, NY _bSpringer International Publishing _c2016
300			_a1 recurso en línea (X, 313 páginas) _b85 ilustraciones, 58 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aMethods in Molecular Biology _x1940-6029 _v1362
505	0		_aIntroduction to Proteomics Technologies -- Topics in Study Design and Analysis for Multi-Stage Clinical Proteomics Studies -- Preprocessing and Analysis of LC-MS-Based Proteomic Data -- Normalization of Reverse Phase Protein Microarray Data: Choosing the Best Normalization Analyte -- Outlier Detection for Mass Spectrometric Data -- Visualization and Differential Analysis of Protein Expression Data Using R -- False Discovery Rate Estimation in Proteomics -- A Nonparametric Bayesian Model for Nested Clustering -- Set-Based Test Procedures for the Functional Analysis of Protein Lists from Differential Analysis -- Classification of Samples with Order Restricted Discriminant Rules -- Application of Discriminant Analysis and Cross Validation on Proteomics Data -- Protein Sequence Analysis by Proximities -- Statistical Method for Integrative Platform Analysis: Application to Integration of Proteomic and Microarray Data -- Data Fusion in Metabolomics and Proteomics for Biomarkers Discovery -- Reconstruction of Protein Networks Using Reverse Phase Protein Array Data -- Detection of Unknown Amino Acid Substitutions Using Error-Tolerant Database Search -- Data Analysis Strategies for Protein Modification Identification -- Dissecting the iTRAQ Data Analysis -- Statistical Aspects in Proteomic Biomarker Discovery.
520			_aThis valuable collection aims to provide a collection of frequently used statistical methods in the field of proteomics. Although there is a large overlap between statistical methods for the different 'omics' fields, methods for analyzing data from proteomics experiments need their own specific adaptations. To satisfy that need, Statistical Analysis in Proteomics focuses on the planning of proteomics experiments, the preprocessing and analysis of the data, the integration of proteomics data with other high-throughput data, as well as some special topics. Written for the highly successful Methods in Molecular Biology series, the chapters contain the kind of detail and expert implementation advice that makes for a smooth transition to the laboratory.   Practical and authoritative, Statistical Analysis in Proteomics serves as an ideal reference for statisticians involved in the planning and analysis of proteomics experiments, beginners as well as advanced researchers, and also for biologists, biochemists, and medical researchers who want to learn more about the statistical opportunities in the analysis of proteomics data.
700	1		_aJung, Klaus _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9781493931057
776	0	8	_iPrinted edition: _z9781493931071
776	0	8	_iPrinted edition: _z9781493979875
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-4939-3106-4 _z(usuarios Universidad Europea de Valencia)
942			_2lcc _cLE
988			_aSpringer_Protocols_2016
999			_c235737 _d235737