000			03958nam a22003975i 4500
999			_c391286 _d391286
001			391286
003			ES-MaUEC
005			20230603125447.0
006			a|||| o|||| 00| 0
007			cr nn 008mamaa
008			230603s2021 xxu| o |||| 0|eng d
020			_a9781071616413
024	7		_a10.1007/978-1-0716-1641-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQP551 _b2021 EB
245	0	0	_aProteomics Data Analysis _cedited by Daniela Cecconi
250			_a1st edition 2021
264		1	_aNew York, NY _bSpringer International Publising _c2021
300			_a1 recurso en línea (XIII, 326 páginas) _b54 ilustraciones, 46 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 _v2361
505	0		_aTwo-Dimensional Gel Electrophoresis Image Analysis -- Chemometric Tools for 2D-PAGE Data Analysis -- Software Options for the Analysis of MS Proteomic Data -- Analysis of Label-Based Quantitative Proteomics Data Using IsoProt -- Quantification of Changes in Protein Expression Using SWATH Proteomics.-Data Processing and Analysis for DIA-Based Phosphoproteomics Using Spectronaut -- Enhanced Glycopeptide Identification Using a GlyConnect Compozitor-Derived Glycan Composition File -- Elaboration Pipeline for the Management of MALDI-MS Imaging Datasets -- Features Selection and Extraction in Statistical Analysis of Proteomics Datasets -- ORA, FCS, and PT Strategies in Functional Enrichment Analysis -- A Strategy for the Annotation and GO Enrichment Analysis of a List of Differentially Expressed Proteins Using ProteoRE -- Protein Subcellular Localization Prediction -- Protein Secretion Prediction Tools and Extracellular Vesicles Databases -- Databases for Protein-Protein Interactions -- Machine and Deep Learning for Prediction of Subcellular Localization -- Deep Learning for Protein-Protein Interaction Site Prediction -- Integrative Analysis of Incongruous Cancer Genomics and Proteomics Datasets -- Integration of Proteomics and Other Omics Data.
520			_aThis thorough book collects methods and strategies to analyze proteomics data. It is intended to describe how data obtained by gel-based or gel-free proteomics approaches can be inspected, organized, and interpreted to extrapolate biological information. Organized into four sections, the volume explores strategies to analyze proteomics data obtained by gel-based approaches, different data analysis approaches for gel-free proteomics experiments, bioinformatic tools for the interpretation of proteomics data to obtain biological significant information, as well as methods to integrate proteomics data with other omics datasets including genomics, transcriptomics, metabolomics, and other types of data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will ensure high quality results in the lab. Authoritative and practical, Proteomics Data Analysis serves as an ideal guide to introduce researchers, both experienced and novice, to new tools and approaches for data analysis to encourage the further study of proteomics. Chapter 16 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
988			_aSpringer_Protocols_2021
650		7	_2embne _9430647 _aProteómica
776	0	8	_iPrinted edition: _z9781071616406
776	0	8	_iPrinted edition: _z9781071616420
776	0	8	_iPrinted edition: _z9781071616437
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-0716-1641-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b06/2023 _dz _eIG _zSI