A Practical Approach to Microarray Data Analysis

By: Daniel P. Berrar (Editor), Werner Dubitzky (Editor), Martin Granzow (Editor)

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Hardcover | 31 December 2002

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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.

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Non-Fiction Science Biology, Life Sciences Biochemistry Life Sciences in General Genetics excluding Medical DNA & Genome Science in General Medicine Other Branches of Medicine Pathology Computing & I.T.

Information Technology General Issue

Acknowledgements	p. vii
Preface	p. ix
Introduction to Microarray Data Analysis	p. 1
Data Pre-Processing Issues in Microarray Analysis	p. 47
Missing Value Estimation	p. 65
Normalization	p. 76
Singular Value Decomposition and Principal Component Analysis	p. 91
Feature Selection in Microarray Analysis	p. 110
Introduction to Classification in Microarray Experiments	p. 132
Bayesian Network Classifiers for Gene Expression Analysis	p. 150
Classifying Microarray Data Using Support Vector Machines	p. 166
Weighted Flexible Compound Covariate Method for Classifying Microarray Data	p. 186
Classification of Expression Patterns Using Artificial Neural Networks	p. 201
Gene Selection and Sample Classification Using a Genetic Algorithm and k-Nearest Neighbor Method	p. 216
Clustering Genomic Expression Data: Design and Evaluation Principles	p. 230
Clustering or Automatic Class Discovery: Hierarchical Methods	p. 246
Discovering Genomic Expression Patterns with Self-Organizing Neural Networks	p. 261
Clustering or Automatic Class Discovery: non-hierarchical, non-SOM	p. 274
Correlation and Association Analysis	p. 289
Global Functional Profiling of Gene Expression Data	p. 306
Microarray Software Review	p. 326
Microrray Analysis as a Process	p. 345
Index	p. 361
Table of Contents provided by Syndetics. All Rights Reserved.

A Practical Approach to Microarray Data Analysis

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