Methods of Microarray Data Analysis

1. Front Matter

   Pages i-xiv

   PDF

2. Introduction

   1. Introduction

      • Simon M. Lin, Kimberly F. Johnson

      Pages 1-3

3. Reviews and Tutorials

   1. Data Mining and Machine Learning Methods for Microarray Analysis

      • Werner Dubitzky, Martin Granzow, Daniel Berrar

      Pages 5-22

   2. Evolutionary Computation in Microarray Data Analysis

      • Jason H. Moore, Joel S. Parker

      Pages 23-35

4. Best Presentation — CAMDA ’00

   1. Using Non-Parametric Methods in the Context of Multiple Testing to Determine Differentially Expressed Genes

      • Gregory Grant, Elisabetta Manduchi, Christian Stoeckert Jr.

      Pages 37-55

5. Quality Analysis and Data Normalization of Spotted Arrays

   1. Iterative Linear Regresssion by Sector

      • 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. A Method to Improve Detection of Disease Using Selectively Expressed Genes in Microarray Data

      • Virginie Aris, Michael Recce

      Pages 69-80

   2. Computational Analysis of Leukemia Microarray Expression Data Using the GA/KNN Method

      • Leping Li, Lee. G. Pedersen, Thomas A. Darden, Clarice R. Weinberg

      Pages 81-95

   3. Classical Statistical Approaches to Molecular Classification of Cancer from Gene Expression Profiling

      • Jun Lu, Sarah Hardy, Wen-Li Tao, Spencer Muse, Bruce Weir, Susan Spruill

      Pages 97-107

   4. Classification of Acute Leukemia Based on DNA Microarray Gene Expressions Using Partial Least Squares

      • Danh V. Nguyen, David M. Rocke

      Pages 109-124

   5. Applying Classification Separability Analysis to Microarray Data

      • Zhen Zhang, Grier Page, Hong Zhang

      Pages 125-136

   6. How Many Genes are Needed for a Discriminant Microarray Data Analysis

      • Wentian Li, Yaning Yang

      Pages 137-149

7. Machine Learning Techniques

   1. Comparing Symbolic and Subsymbolic Machine Learning Approaches to Classification of Cancer and Gene Identification

      • Werner Dubitzky, Martin Granzow, Daniel Berrar

      Pages 151-165

   2. Applying Machine Learning Techniques to Analysis of Gene Expression Data: Cancer Diagnosis

      • Kyu-Baek Hwang, Dong-Yeon Cho, Sang-Wook Park, Sung-Dong Kim, Byoung-Tak Zhang

      Pages 167-182

8. Back Matter

   Pages 183-189

   PDF

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

• Duke University Medical Center, USA

  Simon M. Lin, Kimberly F. Johnson

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.

Policies and ethics