Methods of Microarray Data Analysis II

by Lin, Simon M.; Johnson, Kimberly F.

• ISBN13: 9781402071119
• ISBN10: 1402071116
• Format: Hardcover
• Copyright: 2002-06-01
• Publisher: Kluwer Academic Pub

Summary

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

Contributors	vii
Acknowledgements	ix
Preface	xi
Introduction	1	(8)
An Introduction to DNA Microarrays	9	(14)
Patrick McConnell
Kimberly Johnson
David J. Lockhart
Experimental Design for Gene Microarray Experiments and Differential Expression Analysis	23	(20)
G.V. Bobashev
S. Das
A. Das
Microarray Data Processing and Analysis	43	(22)
Joaquin Dopazo
Biology-Driven Clustering of Microarray Data	65	(16)
Kevin R. Coombes
Keith A. Baggerly
David N. Stivers
Jing Wang
David Gold
Hsi-Guang Sung
Sang-Joon Lee
Extracting Global Structure from Gene Expression Profiles	81	(10)
Charless Fowlkes
Qun Shan
Serge Belongie
Jitendra Malik
Supervised Neural Networks for Clustering Conditions in DNA Array Data After Reducing Noise by Clustering Gene Expression Profiles	91	(14)
Alvaro Mateos
Javier Herrero
Javier Tamames
Joaquin Dopazo
Bayesian Decomposition Analysis of Gene Expression in Yeast Deletion Mutants	105	(18)
Ghislain Bidaut
Thomas D. Moloshok
Jeffrey D. Grant
Frank J. Manion
Michael F. Ochs
Using Functional Genomic Units to Corroborate User Experiments with the Rosetta Compendium	123	(16)
Simon M. Lin
Xuejun Liao
Patrick McConnell
Korkut Vata
Lawrence Carin
Pascal Goldschmidt
Fishing Expedition - A Supervised Approach to Extract Patterns from a Compendium of Expression Profiles	139	(12)
Zhen Zhang
Grier Page
Hong Zhang
Modeling Pharmacogenomics of the NCI-60 Anticancer Data Set: Utilizing Kernel Pls to Correlate the Microarray Data to Therapeutic Responses	151	(18)
Nilanjan Dasgupta
Simon M. Lin
Lawrence Carin
Analysis of Gene Expression Profiles and Drug Activity Patterns by Clustering and Bayesian Network Learning	169	(16)
Jeong-Ho Chang
Kyu-Baek Hwang
Byoung-Tak Zhang
Evaluation of Current Methods of Testing Differential Gene Expression and Beyond	185	(10)
Yi-Ju Li
Ling Zhang
Marcy C. Speer
Eden R. Martin
Extracting Knowledge from Genomic Experiments by Incorporating the Biomedical Literature	195	(18)
James P. Sluka
Index	213