Methods of Microarray Data Analysis

Papers from Camda '00

By: Simon M. Lin, Kimberly F. Johnson

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Hardcover | 30 November 2001

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208 Pages

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23.5 x 15.88 x 1.27

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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 is one of the first books 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 focuses on two well-known data sets, 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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Contributors
Acknowledgements
Preface
Introduction	p. 1
Data Mining and Machine Learning Methods for Microarray Analysis	p. 5
Evolutionary Computation in Microarray Data Analysis	p. 23
Using Non-Parametric Methods in the Context of Multiple Testing to Determine Differentially Expressed Genes	p. 37
Iterative Linear Regression by Sector	p. 57
A Method to Improve Detection of Disease Using Selectively Expressed Genes in Microarray Data	p. 69
Computational Analysis of Leukemia Microarray Expression Data Using the GA/KNN Method	p. 81
Classical Statistical Approaches to Molecular Classification of Cancer from Gene Expression Profiling	p. 97
Classification of Acute Leukemia Based on DNA Microarray Gene Expressions Using Partial Least Squares	p. 109
Applying Classification Separability Analysis to Microarray Data	p. 125
How Many Genes are Needed for a Discriminant Microarray Data Analysis	p. 137
Comparing Symbolic and Subsymbolic Machine Learning Approaches to Classification of Cancer and Gene Identification	p. 151
Applying Machine Learning Techniques to Analysis of Gene Expression Data: Cancer Diagnosis	p. 167
Glossary	p. 183
Index	p. 187
Table of Contents provided by Blackwell. All Rights Reserved.

Methods of Microarray Data Analysis

Papers from Camda '00

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Hardcover

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