Microarray Data Analysis / edited by Giuseppe Agapito.

Format:

Book

Contributor:

Agapito, Giuseppe., Editor.

SpringerLink (Online service)

Series:

Springer Protocols (Springer-12345)

Methods in molecular biology 1940-6029 ; 2401

Methods in Molecular Biology, 1940-6029 ; 2401

Language:

English

Subjects (All):

Biology-Technique.

Genetics.

Bioinformatics.

Quantitative research.

Genetic Techniques.

Data Analysis and Big Data.

Local Subjects:

Genetic Techniques.

Bioinformatics.

Data Analysis and Big Data.

Physical Description:

1 online resource (XI, 317 pages) : 71 illustrations, 54 illustrations in color.

Edition:

1st ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

New York, NY : Springer US : Imprint: Humana, 2022.

System Details:

text file PDF

Summary:

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.

Contents:

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

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.

Other Format:

Printed edition:

ISBN:

978-1-0716-1839-4

9781071618394

Access Restriction:

Restricted for use by site license.