Overview

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.

Full Product Details

Author:   Giuseppe Agapito
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   1st ed. 2022
Volume:   2401
Weight:   0.633kg
ISBN:  

9781071618417

ISBN 10:   1071618415
Pages:   317
Publication Date:   15 December 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1. Tools in Pharmacogenomics Biomarker Identification for Cancer Patients             Francesca Scionti, Maria Teresa Di Martino, Daniele Caracciolo, Licia Pensabene, Pierosandro Tagliaferri, and Mariamena Arbitrio   2. High Performance Framework to Analyze Microarray Data             Fabrizio Marozzo and Loris Belcastro   3. Web and Cloud Computing to Analyze Microarray Data             Barbara Calabrese   4. A Microarray Analysis Technique Using a Self-Organizing Multi-Agent Approach             Agostino Forestiero, Giuseppe Papuzzo, Rosaria De Simone, and Rosa Varchera   5. Improving Analysis and Annotation of Microarray Data with Protein Interactions             Max Kotlyar, Serene W.H. Wong, Chiara Pastrello, and Igor Jurisica   6. Algorithms to Preprocess Microarray Image Data             Paolo Zaffino and Maria Francesca Spadea   7. Microarray Data Preprocessing: From Experimental Design to Differential Analysis             Antonio Federico, Laura Aliisa Saarimäki, Angela Serra, Giusy del Giudice, Pia Anneli Sofia Kinaret, Giovanni Scala, and Dario Greco   8. Supervised Methods for Biomarker Detection from Microarray Experiments             Angela Serra, Luca Cattelani, Michele Fratello, Vittorio Fortino, Pia Anneli Sofia Kinaret, and Dario Greco   9. Unsupervised Algorithms for Microarray Sample Stratification             Michele Fratello, Luca Cattelani, Antonio Federico, Alisa Pavel, Giovanni Scala, Angela Serra, and Dario Greco   10. Pathway Enrichment Analysis of Microarray Data             Chiara Pastrello, Yun Niu, and Igor Jurisica   11. Network Analysis of Microarray Data             Alisa Pavel, Angela Serra, Luca Cattelani, Antonio Federico, and Dario Greco   12. geneExpressionFromGEO: An R Package to Facilitate Data Reading from Gene Expression Omnibus (GEO)             Davide Chicco   13. Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19-Related Studies             Anna Bernasconi and Silvia Cascianelli   14. Alignment of Microarray Data             Francesco Cauteruccio   15. Integration of DNA Microarray with Clinical and Genomic Data             Francesca Scionti, Mariamena Arbitrio, Daniele Caracciolo, Licia Pensabene, Pierfrancesco Tassone, Pierosandro Tagliaferri, and Maria Teresa Di Martino   16. Clustering Methods for Microarray Data Sets             Giuseppe Agapito and Giuseppe Fedele   17. Microarray Data Analysis Protocol             Giuseppe Agapito and Mariamena Arbitrio   18. Using Gene Ontology to Annotate and Prioritize Microarray Data             Marianna Milano   19. Using MMRFBiolinks R-Package for Discovering Prognostic Markers in Multiple Myeloma             Marzia Settino and Mario Cannataro

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