Microarray Data Analysis
Published on 15. December 2022
Book
Paperback/Softback
XI, 317 pages
978-1-0716-1841-7 (ISBN)
Description
This 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.
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.
More details
Series
Edition
2022 ed.
Language
English
Place of publication
New York
United States
Publishing group
Springer-Verlag New York Inc.
Target group
Professional and scholarly
Illustrations
17 s/w Abbildungen, 54 farbige Abbildungen
XI, 317 p. 71 illus., 54 illus. in color.
Dimensions
Height: 254 mm
Width: 178 mm
Thickness: 19 mm
Weight
626 gr
ISBN-13
978-1-0716-1841-7 (9781071618417)
DOI
10.1007/978-1-0716-1839-4
Schweitzer Classification
Other editions
Additional editions
Content
Tools 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.