Methods of Microarray Data Analysis II: Papers from CAMDA’ 01

✍ Scribed by Simon M. Lin, Kimberly F. Johnson (eds.)

• Publisher: Springer US
• Year: 2002
• Tongue: English
• Leaves: 214
• Edition: 1
• Category: Library

✦ Synopsis

Microarray technology is a major experimental tool for functional genomic explorations, and will continue to be a major tool throughout this decade and beyond. The recent explosion of this technology threatens to overwhelm the scientific community with massive quantities of data. Because microarray data analysis is an emerging field, very few analytical models currently exist. Methods of Microarray Data Analysis II is the second book in this pioneering series dedicated to this exciting new field. In a single reference, readers can learn about the most up-to-date methods, ranging from data normalization, feature selection, and discriminative analysis to machine learning techniques.

Currently, there are no standard procedures for the design and analysis of microarray experiments. Methods of Microarray Data Analysis II focuses on a single data set, using a different method of analysis in each chapter. Real examples expose the strengths and weaknesses of each method for a given situation, aimed at helping readers choose appropriate protocols and utilize them for their own data set. In addition, web links are provided to the programs and tools discussed in several chapters. This book is an excellent reference not only for academic and industrial researchers, but also for core bioinformatics/genomics courses in undergraduate and graduate programs.

✦ Table of Contents

Introduction....Pages 1-7
An Introduction to DNA Microarrays....Pages 9-21
Experimental Design for Gene Microarray Experiments and Differential Expression Analysis....Pages 23-41
Microarray Data Processing and Analysis....Pages 43-63
Biology-driven Clustering of Microarray Data....Pages 65-79
Extracting Global Structure from Gene Expression Profiles....Pages 81-90
Supervised Neural Networks for Clustering Conditions in DNA Array Data After Reducing Noise by Clustering Gene Expression Profiles....Pages 91-103
Bayesian Decomposition Analysis of Gene Expression in Yeast Deletion Mutants....Pages 105-122
Using Functional Genomic Units to Corroborate User Experiments with the Rosetta Compendium....Pages 123-137
Fishing Expedition - a Supervised Approach to Extract Patterns from a Compendium of Expression Profiles....Pages 139-149
Modeling Pharmacogenomics of the NCI-60 Anticancer Data Set: Utilizing Kernel Pls to Correlate the Microarray Data to Therapeutic Responses....Pages 151-167
Analysis of Gene Expression Profiles and Drug Activity Patterns by Clustering and Bayesian Network Learning....Pages 169-184
Evaluation of Current Methods of Testing Differential Gene expression and Beyond....Pages 185-194
Extracting Knowledge from Genomic Experiments by Incorporating the Biomedical Literature....Pages 195-209

✦ Subjects

Biochemistry, general; Human Genetics; Statistics for Life Sciences, Medicine, Health Sciences