Methods of Microarray Data Analysis II: Papers from CAMDA '01 / Edition 1

Methods of Microarray Data Analysis II: Papers from CAMDA '01 / Edition 1

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Springer US

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Methods of Microarray Data Analysis II: Papers from CAMDA '01 / Edition 1

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.

Product Details

ISBN-13: 9781402071119
Publisher: Springer US
Publication date: 06/30/2002
Edition description: 2002
Pages: 214
Product dimensions: 6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

Contributors. Acknowledgements. Preface. Introduction.
An Introduction to DNA Microarrays; P. McConnell, et al.
Experimental Design for Gene Microarray Experiments and Differential Expression Analysis; G.V. Bobashev, et al.
Microarray Data Processing and Analysis; J. Dopazo.
Biology-Driven Clustering of Microarray Data; K.R. Coombes, et al.
Extracting Global Structure from Gene Expression Profiles; C. Fowlkes, et al.
Supervised Neural Networks for Clustering Conditions in DNA Array Data after Reducing Noise by Clustering Gene Expression Profiles; A. Mateos, et al.
Bayesian Decomposition Analysis of Gene Expression in Yeast Deletion Mutants; G. Bidaut, et al.
Using Functional Genomic Units to Corroborate User Experiments with the Rosetta Compendium; S.M. Lin, et al.
Fishing Expedition - A Supervised Approach to Extract Patterns from a Compendium of Expression Profiles; Z. Zhang, et al.
Modeling Pharmacogenomics of the NCI-60 Anticancer Data Set: Utilizing Kernel PLS to Correlate the Microarray Data to Therapeutic Responses; N. Dasgupta, et al.
Analysis of Gene Expression Profiles and Drug Activity Patterns by Clustering and Bayesian Network Learning; Jeong-Ho Chang, et al.
Evaluation of Current Methods of Testing Differential Gene Expression and Beyond; Y.-J. Li, et al.
Extracting Knowledge from Genomic Experiments by Incorporating the Biomedical Literature; J.P. Sluka.

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