This book provides a comprehensive, interdisciplinary collection of the main, up-to-date methods, tools, and techniques for microarray data analysis, covering the necessary steps for the acquisition of the data, its preprocessing, and its posterior analysis. Featuring perspectives from biology, computer science, and statistics, the volume explores machine learning methods such as clustering, feature selection, classification, data normalization, and missing value imputation, as well as the statistical analysis of the data and the most popular computer tools to analyze microarray data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will aid researchers in getting successful results.

Cutting-edge and authoritative, Microarray Bioinformatics serves as an ideal guide for researchers and graduate students in bioinformatics, with basic knowledge in biology and computer science, and with a view to work with microarray datasets.

1. Introduction to bioinformatics.- 2. Protocol for DNA microarrays on glass slides.- 3. Data warehousing with TargetMine for omics data analysis.- 4. A review of microarray datasets: where to find them and specific characteristics.- 5. Statistical analysis of microarray data.- 6. Feature selection applied to microarray data.- 7. Cluster analysis of microarray data.- 8. Classification of microarray data.- 9. Microarray data normalization and robust detection of rhythmic features.- 10. HPC tools to deal with microarray data.- 11. ROC curves for the statistical analysis of microarray data.- 12. Missing values imputation algorithms for microarray gene expression data.- 13. Computer tools to analyze microarray data.- 14. Challenges and future trends for microarray analysis.