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A Practical Approach to Microarray Data Analysis

  • Daniel P. Berrar
  • Werner Dubitzky
  • Martin Granzow

Table of contents

  1. Front Matter
    Pages i-xv
  2. Werner Dubitzky, Martin Granzow, C. Stephen Downes, Daniel Berrar
    Pages 1-46
  3. Nicholas A. Tinker, Laurian S. Robert, Gail Butler, Linda J. Harris
    Pages 47-64
  4. Olga G. Troyanskaya, David Botstein, Russ B. Altman
    Pages 65-75
  5. Norman Morrison, David C. Hoyle
    Pages 76-90
  6. Michael E. Wall, Andreas Rechtsteiner, Luis M. Rocha
    Pages 91-109
  7. Eric P. Xing
    Pages 110-131
  8. Sandrine Dudoit, Jane Fridly
    Pages 132-149
  9. Byoung-Tak Zhang, Kyu-Baek Hwang
    Pages 150-165
  10. Markus Ringnér, Patrik Edén, Peter Johansson
    Pages 201-215
  11. Francisco Azuaje, Nadia Bolshakova
    Pages 230-245
  12. Derek C. Stanford, Douglas B. Clarkson, Antje Hoering
    Pages 246-260
  13. Simon M. Lin, Kimberly F. Johnson
    Pages 289-305
  14. Sorin Draghici, Stephen A. Krawetz
    Pages 306-325
  15. Yuk Fai Leung, Dennis Shun Chiu Lam, Chi Pui Pang1
    Pages 326-344
  16. Susan Jensen
    Pages 345-360
  17. Back Matter
    Pages 361-368

About this book

Introduction

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.

Keywords

Bayesian network Evaluation algorithms bioinformatics classification computer science data mining gene expression genes genetic algorithms

Editors and affiliations

  • Daniel P. Berrar
    • 1
  • Werner Dubitzky
    • 2
  • Martin Granzow
    • 3
  1. 1.School of Biomedical SciencesUniversity of Ulster at ColeraineNorthern Ireland
  2. 2.Faculty of Life and Health Science and Faculty of InformaticsUniversity of Ulster at ColeraineNorthern Ireland
  3. 3.4T2consultingWeingartenGermany

Bibliographic information

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