Methods of Microarray Data Analysis

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

• ISBN13: 9780792375647
• ISBN10: 0792375645
• Format: Hardcover
• Copyright: 2001-10-01
• 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 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.

Author Biography

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.

Table of Contents

Contributors	ix
Acknowledgements	xi
Preface	xiii
Introduction	1	(4)
Reviews and Tutorials
Data Mining and Machine Learning Methods For Microarray Analysis	5	(18)
Werner Dubitzky
Martin Granzow
Daniel Berrar
Evolutionary Computation in Microarray Data Analysis	23	(14)
Jason H. Moore
Joel S. Parker
Best Presentation --- CAMDA '00
Using Non-Parametric Methods in the Context of Multiple Testing to Determine Differentially Expressed Genes	37	(20)
Gregory Grant
Elisabetta Manduchi
Christian Stoeckert, Jr.
Quality Analysis and Data Normalization of Spotted Arrays
Iterative Linear Regression By Sector	57	(12)
David B. Finkelstein
Rob Ewing
Jeremy Gollub
Fredrik Sterky
Shauna Somerville
J. Michael Cherry
Feature Selection, Dimension Reduction, and Discriminative Analysis
A Method To Improve Detection of Disease Using Selectively Expressed Genes in Microarray Data	69	(12)
Virginie Aris
Michael Recce
Computational Analysis of Leukemia Microarray Expression Data Using The GA/KNN Method	81	(16)
Leping Li
Lee. G. Pedersen
Thomas A. Darden
Clarice R. Weinberg
Classical Statistical Approaches To Molecular Classification of Cancer From Gene Expression Profiling	97	(12)
Jun Lu
Sarah Hardy
Wen-Li Tao
Spencer Muse
Bruce Weir
Susan Spruill
Classification of Acute Leukemia Based on DNA Microarray Gene Expressions Using Partial Least Squares	109	(16)
Danh V. Nguyen
David M. Rocke
Applying Classification Separability Analysis to Microarray Data	125	(12)
Zhen Zhang
Grier Page
Hong Zhang
How Many Genes Are Needed For A Discriminant Microarray Data Analysis	137	(14)
Wentian Li
Yaning Yang
Machine Learning Techniques
Comparing Symbolic and Subsymbolic Machine Learning Approaches to Classification of Cancer and Gene Identification	151	(16)
Werner Dubitzky
Martin Granzow
Daniel Berrar
Applying Machine Learning Techniques to Analysis of Gene Expression Data: Cancer Diagnosis	167	(16)
Kyu-Baek Hwang
Dong-Yeon Cho
Sang-Wook Park
Sung-Dong Kim
Byoung-Tak Zhang
Glossary	183	(4)
Index	187