A Practical Approach to Microarray Data Analysis

Acknowledgements. Preface. 1. Introduction to Microarray Data Analysis. 2. Data Pre-Processing Issues in Microarray Analysis. 3. Missing Value Estimation. 4. Normalization. 5. Singular Value Decomposition and Principal Component Analysis. 6. Feature Selection in Microarray Analysis. 7. Introduction to Classification in Microarray Experiments. 8. Bayesian Network Classifiers for Gene Expression Analysis. 9. Classifying Microarray Data Using Support Vector Machines. 10. Weighted Flexible Compound Covariate Method for Classifying Microarray Data. 11. Classification of Expression Patterns Using Artificial Neural Networksl. 12. Gene Selection and Sample Classification Using a Genetic Algorithm and k-Nearest Neighbor Method. 13. Clustering Genomic Expression Data: Design and Evaluation Principles. 14. Clustering or Automatic Class Discovery: Hierarchical Methods. 15. Discovering Genomic Expression Patterns with Self-Organizing Neural Networks. 16. Clustering or Automatic Class Discovery: non-hierarchical, non-SOM. 17. Correlation and Association Analysis. 18. Global Functional Profiling of Gene Expression Data. 19. Microarray Software Review. 20. Microarray Analysis as a Process. Index.