Microarray Data Analysis

ISBN-13: 9781071618417 / Angielski / Miękka / 2022 / 317 str.

Giuseppe Agapito

Opis

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.

Spis treści

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