A Practical Approach to Microarray Data Analysis

Acknowledgements. Preface. 1. Introduction to Microarray Data Analysis; W. Dubitzky, et al. 2. Data Pre-Processing Issues in Microarray Analysis; N.A. Tinker, et al. 3. Missing Value Estimation; O.G. Troyanskaya, et al. 4. Normalization; N. Morrison, D.C. Hoyle. 5. Singular Value Decomposition and Principal Component Analysis; M.E. Wall, et al. 6. Feature Selection in Microarray Analysis; E.P. Xing. 7. Introduction to Classification in Microarray Experiments; S. Dudoit, J. Fridlyand. 8. Bayesian Network Classifiers for Gene Expression Analysis; B.-T. Zhang, K.-B. Hwang. 9. Classifying Microarray Data Using Support Vector Machines; S. Mukherjee. 10. Weighted Flexible Compound Covariate Method for Classifying Microarray Data; Y. Shyr, K.M. Kim. 11. Classification of Expression Patterns Using Artificial Neural Networks; M. Ringnér, et al. 12. Gene Slection and Sample Classification Using a Genetic Algorithm and k-Nearest Neighbor Method. 13. Clustering Genomic Expression Data: Design and Evaluation Principles; F. Azuaje, N. Bolshakova. 14. Clustering or Automatic Class Discovery: Hierarchical Methods; D.C. Stanford, et al. 15. Discovering Genomic Expression Patterns with Self-Organizing Neural Networks; F. Azuaje. 16. Clustering or Automatic Class Discovery: non-hierarchical, non-SOM; K.Y. Yeung. 17. Correlation and Association Analysis; S.M. Lin, K.F. Johnson. 18. Global Functional Profiling of Gene Expression Data; S. Draghici, S.A. Krawetz. 19.Microarray Software Review; Y.F. Leung, et al. 20. Microarray Analysis as a Process; S. Jensen. Index.