A Practical Approach to Microarray Data Analysis

ISBN-13: 9781441912268 / Angielski / Miękka / 2009 / 368 str.

Daniel P. Berrar; Werner Dubitzky; Martin Granzow

Opis

A Practical Approach to Microarray Data Analysis is for all life scientists, statisticians, computer experts, technology developers, managers, and other professionals tasked with developing, deploying, and using microarray technology including the necessary computational infrastructure and analytical tools. 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.
Key topics covered include:

-Format of result from data analysis, analytical modeling/experimentation;
-Validation of analytical results;
-Data analysis/Modeling task;
-Analysis/modeling tools;
-Scientific questions, goals, and tasks;
-Application;
-Data analysis methods;
-Criteria for assessing analysis methodologies, models, and tools.

Spis treści

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 Selection 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.