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.
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.
Inhaltsverzeichnis
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
Klappentext
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.
n
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.
Includes cutting-edge techniques
Provides step-by-step detail essential for reproducible results
Contain key implementation advice from the experts
