Methods of Microarray Data Analysis

Papers from CAMDA ’00

  • Simon M. Lin
  • Kimberly F. Johnson

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Introduction

    1. Simon M. Lin, Kimberly F. Johnson
      Pages 1-3
  3. Reviews and Tutorials

    1. Werner Dubitzky, Martin Granzow, Daniel Berrar
      Pages 5-22
    2. Jason H. Moore, Joel S. Parker
      Pages 23-35
  4. Best Presentation — CAMDA ’00

    1. Gregory Grant, Elisabetta Manduchi, Christian Stoeckert Jr.
      Pages 37-55
  5. Quality Analysis and Data Normalization of Spotted Arrays

    1. David B. Finkelstein, Rob Ewing, Jeremy Gollub, Fredrik Sterky, Shauna Somerville, J. Michael Cherry
      Pages 57-67
  6. Feature Selection, Dimension Reduction, and Discriminative Analysis

    1. Leping Li, Lee. G. Pedersen, Thomas A. Darden, Clarice R. Weinberg
      Pages 81-95
    2. Jun Lu, Sarah Hardy, Wen-Li Tao, Spencer Muse, Bruce Weir, Susan Spruill
      Pages 97-107
    3. Zhen Zhang, Grier Page, Hong Zhang
      Pages 125-136
  7. Machine Learning Techniques

    1. Kyu-Baek Hwang, Dong-Yeon Cho, Sang-Wook Park, Sung-Dong Kim, Byoung-Tak Zhang
      Pages 167-182
  8. Back Matter
    Pages 183-189

About this book


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.


DNA Microarray bioinformatics classification data analysis data mining evolution gene expression genes machine learning

Editors and affiliations

  • Simon M. Lin
    • 1
  • Kimberly F. Johnson
    • 1
  1. 1.Duke University Medical CenterUSA

Bibliographic information

Industry Sectors
Chemical Manufacturing
Health & Hospitals
Consumer Packaged Goods