A Practical Approach to Microarray Data Analysis

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