Publisher's Synopsis
Table of Contents Part I: Genome Probing Using Microarrays 1. Introduction 2. DNA, RNA, Proteins, and Gene Expression 3. Microarray Technology 4. Inherent Variability in Array Data 5. Background Noise 6. Transformation and Normalization 7. Missing Values in Array Data 8. Saturated Intensity Readings Part II: Statistical Models and Analysis 9. Experimental Design 10. ANOVA Models for Microarray Data 11. Multiple Testing in Microarray Studies 12. Permutation Tests in Microarray Data 13. Bayesian Methods for Microarray Data 14. Power and Sample Size Considerations Part III. Unsupervised Exploratory Analysis 15. Cluster Analysis 16. Principal Components and Singular Value Decomposition 17. Self-organizing Maps Part IV. Supervised Learning Methods 18. Discrimination and Classification 19. Artificial Neural Networks 20. Support Vector Machines
