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008			211213s2022 xxua o |||| 0|eng d
020			_a9781071618394
024	7		_a10.1007/978-1-0716-1839-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQP624 _b2022 EB
245	0	0	_aMicroarray Data Analysis _cedited by Giuseppe Agapito
250			_a1st edition 2022
264		1	_aNew York, NY _bSpringer International Publising _c2022
300			_a1 recurso en línea (XI, 317 páginas) _b71 ilustraciones, 54 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 _v2401
505	0		_aTools in Pharmacogenomics Biomarker Identification for Cancer Patients -- High Performance Framework to Analyze Microarray Data -- Web and Cloud Computing to Analyze Microarray Data -- A Microarray Analysis Technique Using a Self-Organizing Multi-Agent Approach -- Improving Analysis and Annotation of Microarray Data with Protein Interactions -- Algorithms to Preprocess Microarray Image Data -- Microarray Data Preprocessing: From Experimental Design to Differential Analysis -- Supervised Methods for Biomarker Detection from Microarray Experiments -- Unsupervised Algorithms for Microarray Sample Stratification -- Pathway Enrichment Analysis of Microarray Data -- Network Analysis of Microarray Data -- geneExpressionFromGEO: An R Package to Facilitate Data Reading from Gene Expression Omnibus (GEO) -- Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19-Related Studies -- Alignment of Microarray Data -- Integration of DNA Microarray with Clinical and Genomic Data -- Clustering Methods for Microarray Data Sets -- Microarray Data Analysis Protocol -- Using Gene Ontology to Annotate and Prioritize Microarray Data -- Using MMRFBiolinks R-Package for Discovering Prognostic Markers in Multiple Myeloma.
520			_aThis meticulous book explores the leading methodologies, techniques, and tools for microarray data analysis, given the difficulty of harnessing the enormous amount of data. The book includes examples and code in R, requiring only an introductory computer science understanding, and the structure and the presentation of the chapters make it suitable for use in bioinformatics courses. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of key detail and expert implementation advice that ensures successful results and reproducibility. Authoritative and practical, Microarray Data Analysis is an ideal guide for students or researchers who need to learn the main research topics and practitioners who continue to work with microarray datasets.
988			_aSpringer_Protocols_2022
650		7	_2embne _9140806 _aADN
650		7	_2embne _9162770 _aExpresión génica
776	0	8	_iPrinted edition: _z9781071618387
776	0	8	_iPrinted edition: _z9781071618400
776	0	8	_iPrinted edition: _z9781071618417
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-0716-1839-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b07/2023 _dz _eb _zSI