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Methods of Microarray Data Analysis II

Papers from CAMDA’ 01

  • Simon M. Lin
  • Kimberly F. Johnson

Table of contents

  1. Front Matter
    Pages i-xi
  2. Pages 1-7
  3. Patrick McConnell, Kimberly Johnson, David J. Lockhart
    Pages 9-21
  4. Joaquín Dopazo
    Pages 43-63
  5. Kevin R. Coombes, Keith A. Baggerly, David N. Stivers, Jing Wang, David Gold, Hsi-Guang Sung et al.
    Pages 65-79
  6. Charless Fowlkes, Qun Shan, Serge Belongie, Jitendra Malik
    Pages 81-90
  7. Ghislain Bidaut, Thomas D. Moloshok, Jeffrey D. Grant, Frank J. Manion, Michael F. Ochs
    Pages 105-122
  8. Simon M. Lin, Xuejun Liao, Patrick McConnell, Korkut Vata, Lawrence Carin, Pascal Goldschmidt
    Pages 123-137
  9. Yi-Ju Li, Ling Zhang, Marcy C. Speer, Eden R. Martin
    Pages 185-194
  10. Back Matter
    Pages 211-214

About this book

Introduction

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.

Keywords

Clustering DNA Microarray bioinformatics data analysis gene expression genes machine learning

Editors and affiliations

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

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