Daniel P. Berrar,
Werner Dubitzky,
Martin Granzow
Springer New York
2009e druk, 2009
9781441912268
A Practical Approach to Microarray Data Analysis
Specificaties
Paperback, 368 blz.
|
Engels
Springer New York |
2009e druk, 2009
ISBN13: 9781441912268
Rubricering
Levertijd ongeveer 8 werkdagen
Samenvatting
The book addresses the requirement of scientists and researchers to gain a basic understanding of microarray analysis methodologies and tools. It is intended for students, teachers, researchers, and research managers who want to understand the state of the art and of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. The book is designed to be used by the practicing professional tasked with the design and analysis of microarray experiments or as a text for a senior undergraduate- or graduate level course in analytical genetics, biology, bioinformatics, computational biology, statistics and data mining, or applied computer science.
Specificaties
ISBN13:9781441912268
Taal:Engels
Bindwijze:paperback
Aantal pagina's:368
Uitgever:Springer New York
Druk:2009
Inhoudsopgave
<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>