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.
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Springer Book Archives
An Introduction to DNA Microarrays.- Experimental Design for Gene
Microarray Experiments and Differential Expression Analysis.- Microarray Data
Processing and Analysis.- Biology-driven Clustering of Microarray Data.-
Extracting Global Structure from Gene Expression Profiles.- Supervised Neural
Networks for Clustering Conditions in DNA Array Data After Reducing Noise by
Clustering Gene Expression Profiles.- Bayesian Decomposition Analysis of Gene
Expression in Yeast Deletion Mutants.- Using Functional Genomic Units to
Corroborate User Experiments with the Rosetta Compendium.- Fishing Expedition
- a Supervised Approach to Extract Patterns from a Compendium of Expression
Profiles.- Modeling Pharmacogenomics of the NCI-60 Anticancer Data Set:
Utilizing Kernel Pls to Correlate the Microarray Data to Therapeutic
Responses.- Analysis of Gene Expression Profiles and Drug Activity Patterns
by Clustering and Bayesian Network Learning.- Evaluation of Current Methods
of Testing Differential Gene expression and Beyond.- Extracting Knowledge
from Genomic Experiments by Incorporating the Biomedical Literature.
Simon M. Lin is Manager of Duke Bioinformatics Shared Resource, Duke University Medical Center. Kimberly F. Johnson is Director of Duke Cancer Center Information Systems and Director of Duke Bioinformatics Shared Resource, Duke University Medical Center.
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