Overview
- Editors:
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Daniel P. Berrar
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School of Biomedical Sciences, University of Ulster at Coleraine, Northern Ireland
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Werner Dubitzky
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Faculty of Life and Health Science and Faculty of Informatics, University of Ulster at Coleraine, Northern Ireland
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Martin Granzow
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4T2consulting, Weingarten, Germany
- 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)
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- Werner Dubitzky, Martin Granzow, C. Stephen Downes, Daniel Berrar
Pages 1-46
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- Nicholas A. Tinker, Laurian S. Robert, Gail Butler, Linda J. Harris
Pages 47-64
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- Olga G. Troyanskaya, David Botstein, Russ B. Altman
Pages 65-75
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- Norman Morrison, David C. Hoyle
Pages 76-90
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- Michael E. Wall, Andreas Rechtsteiner, Luis M. Rocha
Pages 91-109
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- Sandrine Dudoit, Jane Fridly
Pages 132-149
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- Byoung-Tak Zhang, Kyu-Baek Hwang
Pages 150-165
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- Markus Ringnér, Patrik Edén, Peter Johansson
Pages 201-215
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- Leping Li, Clarice R. Weinberg
Pages 216-229
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- Francisco Azuaje, Nadia Bolshakova
Pages 230-245
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- Derek C. Stanford, Douglas B. Clarkson, Antje Hoering
Pages 246-260
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- Simon M. Lin, Kimberly F. Johnson
Pages 289-305
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- Sorin Draghici, Stephen A. Krawetz
Pages 306-325
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- Yuk Fai Leung, Dennis Shun Chiu Lam, Chi Pui Pang1
Pages 326-344
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
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School of Biomedical Sciences, University of Ulster at Coleraine, Northern Ireland
Daniel P. Berrar
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Faculty of Life and Health Science and Faculty of Informatics, University of Ulster at Coleraine, Northern Ireland
Werner Dubitzky
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4T2consulting, Weingarten, Germany
Martin Granzow