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Methods of Microarray Data Analysis

Papers from CAMDA ’00

  • Book
  • © 2002

Overview

Editors:
  1. Simon M. Lin

    1. Duke University Medical Center, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Kimberly F. Johnson

    1. Duke University Medical Center, USA

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    You can also search for this editor in PubMed   Google Scholar

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xiv
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  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
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Keywords

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.

Editors and Affiliations

  • Duke University Medical Center, USA

    Simon M. Lin, Kimberly F. Johnson

About the editors

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.

Bibliographic Information

  • Book Title: Methods of Microarray Data Analysis

  • Book Subtitle: Papers from CAMDA ’00

  • Editors: Simon M. Lin, Kimberly F. Johnson

  • DOI: https://doi.org/10.1007/978-1-4615-0873-1

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer Science+Business Media New York 2002

  • Hardcover ISBN: 978-0-7923-7564-7Published: 30 November 2001

  • Softcover ISBN: 978-1-4613-5281-5Published: 31 October 2012

  • eBook ISBN: 978-1-4615-0873-1Published: 06 December 2012

  • Edition Number: 1

  • Number of Pages: XIV, 189

  • Topics: Biochemistry, general, Human Genetics, Statistics for Life Sciences, Medicine, Health Sciences

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