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020 _a9781071618394
_9978-1-0716-1839-4
024 7 _a10.1007/978-1-0716-1839-4
_2doi
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050 4 _aQH442-442.6
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_2bicssc
072 7 _aPDN
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082 0 4 _a570.28
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082 0 4 _a576.5
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245 1 0 _aMicroarray Data Analysis
_h[electronic resource] /
_cedited by Giuseppe Agapito.
250 _a1st ed. 2022.
264 1 _aNew York, NY :
_bSpringer US :
_bImprint: Humana,
_c2022.
300 _aXI, 317 p. 71 illus., 54 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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 -- ClusteringMethods 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.
650 0 _aBiology
_xTechnique.
650 0 _aGenetics.
650 0 _aBioinformatics.
650 0 _aQuantitative research.
650 1 4 _aGenetic Techniques.
650 2 4 _aBioinformatics.
650 2 4 _aData Analysis and Big Data.
700 1 _aAgapito, Giuseppe.
_eeditor.
_0(orcid)0000-0003-2868-7732
_1https://orcid.org/0000-0003-2868-7732
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9781071618387
776 0 8 _iPrinted edition:
_z9781071618400
776 0 8 _iPrinted edition:
_z9781071618417
830 0 _aMethods in Molecular Biology,
_x1940-6029 ;
_v2401
856 4 0 _uhttps://doi.org/10.1007/978-1-0716-1839-4
912 _aZDB-2-PRO
999 _c5630
_d5630