A Practical Approach to Microarray Data Analysis

✍ Scribed by Werner Dubitzky, Martin Granzow (auth.), Daniel P. Berrar, Werner Dubitzky, Martin Granzow (eds.)

• Publisher: Springer US
• Year: 2003
• Tongue: English
• Leaves: 381
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Introduction to Microarray Data Analysis....Pages 1-46
Data Pre-Processing Issues in Microarray Analysis....Pages 47-64
Missing Value Estimation....Pages 65-75
Normalization....Pages 76-90
Singular Value Decomposition and Principal Component Analysis....Pages 91-109
Feature Selection in Microarray Analysis....Pages 110-131
Introduction to Classification in Microarray Experiments....Pages 132-149
Bayesian Network Classifiers for Gene Expression Analysis....Pages 150-165
Classifying Microarray Data Using Support Vector Machines....Pages 166-185
Weighted Flexible Compound Covariate Method for Classifying Microarray Data....Pages 186-200
Classification of Expression Patterns Using Artificial Neural Networks....Pages 201-215
Gene Selection and Sample Classification Using a Genetic Algorithm and k -Nearest Neighbor Method....Pages 216-229
Clustering Genomic Expression Data: Design and Evaluation Principles....Pages 230-245
Clustering or Automatic Class Discovery: Hierarchical Methods....Pages 246-260
Discovering Genomic Expression Patterns with Self-Organizing Neural Networks....Pages 261-273
Clustering or Automatic Class Discovery: Non-Hierarchical, non-SOM....Pages 274-288
Correlation and Association Analysis....Pages 289-305
Global Functional Profiling of Gene Expression Data....Pages 306-325
Microarray Software Review....Pages 326-344
Microarray Analysis as a Process....Pages 345-360

✦ Subjects

Biochemistry, general; Evolutionary Biology; Data Structures, Cryptology and Information Theory; Artificial Intelligence (incl. Robotics); The Computing Profession