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A Practical Approach to Microarray Data Analysis

  • Textbook
  • © 2003

Overview

Editors:
  1. Daniel P. Berrar

    1. School of Biomedical Sciences, University of Ulster at Coleraine, Northern Ireland

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  2. Werner Dubitzky

    1. Faculty of Life and Health Science and Faculty of Informatics, University of Ulster at Coleraine, Northern Ireland

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    You can also search for this editor in PubMed   Google Scholar

  3. Martin Granzow

    1. 4T2consulting, Weingarten, Germany

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  • Addresses the requirement of scientists and researchers to gain a basic understanding of microarray analysis methodologies and tools

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Table of contents (20 chapters)

  1. Front Matter

    Pages i-xv
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  2. Introduction to Microarray Data Analysis

    • Werner Dubitzky, Martin Granzow, C. Stephen Downes, Daniel Berrar
    Pages 1-46

  3. Data Pre-Processing Issues in Microarray Analysis

    • Nicholas A. Tinker, Laurian S. Robert, Gail Butler, Linda J. Harris
    Pages 47-64

  4. Missing Value Estimation

    • Olga G. Troyanskaya, David Botstein, Russ B. Altman
    Pages 65-75

  5. Normalization

    • Norman Morrison, David C. Hoyle
    Pages 76-90

  6. Singular Value Decomposition and Principal Component Analysis

    • Michael E. Wall, Andreas Rechtsteiner, Luis M. Rocha
    Pages 91-109

  7. Feature Selection in Microarray Analysis

    • Eric P. Xing
    Pages 110-131

  8. Introduction to Classification in Microarray Experiments

    • Sandrine Dudoit, Jane Fridly
    Pages 132-149

  9. Bayesian Network Classifiers for Gene Expression Analysis

    • Byoung-Tak Zhang, Kyu-Baek Hwang
    Pages 150-165

  10. Classifying Microarray Data Using Support Vector Machines

    • Sayan Mukherjee
    Pages 166-185

  11. Weighted Flexible Compound Covariate Method for Classifying Microarray Data

    • Yu Shyr, KyungMann Kim
    Pages 186-200

  12. Classification of Expression Patterns Using Artificial Neural Networks

    • Markus Ringnér, Patrik Edén, Peter Johansson
    Pages 201-215

  13. Gene Selection and Sample Classification Using a Genetic Algorithm and k-Nearest Neighbor Method

    • Leping Li, Clarice R. Weinberg
    Pages 216-229

  14. Clustering Genomic Expression Data: Design and Evaluation Principles

    • Francisco Azuaje, Nadia Bolshakova
    Pages 230-245

  15. Clustering or Automatic Class Discovery: Hierarchical Methods

    • Derek C. Stanford, Douglas B. Clarkson, Antje Hoering
    Pages 246-260

  16. Discovering Genomic Expression Patterns with Self-Organizing Neural Networks

    • Francisco Azuaje
    Pages 261-273

  17. Clustering or Automatic Class Discovery: Non-Hierarchical, non-SOM

    • Ka Yee Yeung
    Pages 274-288

  18. Correlation and Association Analysis

    • Simon M. Lin, Kimberly F. Johnson
    Pages 289-305

  19. Global Functional Profiling of Gene Expression Data

    • Sorin Draghici, Stephen A. Krawetz
    Pages 306-325

  20. Microarray Software Review

    • Yuk Fai Leung, Dennis Shun Chiu Lam, Chi Pui Pang1
    Pages 326-344
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Keywords

About this book

In the past several years, DNA microarray technology has attracted tremendous interest in both the scientific community and in industry. With its ability to simultaneously measure the activity and interactions of thousands of genes, this modern technology promises unprecedented new insights into mechanisms of living systems. Currently, the primary applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery (pharmacogenomics), and toxicological research (toxicogenomics). Typical scientific tasks addressed by microarray experiments include the identification of coexpressed genes, discovery of sample or gene groups with similar expression patterns, identification of genes whose expression patterns are highly differentiating with respect to a set of discerned biological entities (e.g., tumor types), and the study of gene activity patterns under various stress conditions (e.g., chemical treatment). More recently, the discovery, modeling, and simulation of regulatory gene networks, and the mapping of expression data to metabolic pathways and chromosome locations have been added to the list of scientific tasks that are being tackled by microarray technology. Each scientific task corresponds to one or more so-called data analysis tasks. Different types of scientific questions require different sets of data analytical techniques. Broadly speaking, there are two classes of elementary data analysis tasks, predictive modeling and pattern-detection. Predictive modeling tasks are concerned with learning a classification or estimation function, whereas pattern-detection methods screen the available data for interesting, previously unknown regularities or relationships.

Editors and Affiliations

  • School of Biomedical Sciences, University of Ulster at Coleraine, Northern Ireland

    Daniel P. Berrar

  • Faculty of Life and Health Science and Faculty of Informatics, University of Ulster at Coleraine, Northern Ireland

    Werner Dubitzky

  • 4T2consulting, Weingarten, Germany

    Martin Granzow

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