Book 2002

Methods of Microarray Data Analysis

Papers from CAMDA ’00

Editors:

ISBN: 978-1-4613-5281-5 (Print) 978-1-4615-0873-1 (Online)

Table of contents (13 chapters)

  1. Front Matter

    Pages i-xiv

  2. Introduction

    1. No Access

      Chapter

      Pages 1-3

      Introduction

  3. Reviews and Tutorials

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      Chapter

      Pages 5-22

      Data Mining and Machine Learning Methods for Microarray Analysis

    2. No Access

      Chapter

      Pages 23-35

      Evolutionary Computation in Microarray Data Analysis

  4. Best Presentation — CAMDA ’00

    1. No Access

      Chapter

      Pages 37-55

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

  5. Quality Analysis and Data Normalization of Spotted Arrays

    1. No Access

      Chapter

      Pages 57-67

      Iterative Linear Regresssion by Sector

  6. Feature Selection, Dimension Reduction, and Discriminative Analysis

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      Chapter

      Pages 69-80

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

    2. No Access

      Chapter

      Pages 81-95

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

    3. No Access

      Chapter

      Pages 97-107

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

    4. No Access

      Chapter

      Pages 109-124

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

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      Chapter

      Pages 125-136

      Applying Classification Separability Analysis to Microarray Data

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      Chapter

      Pages 137-149

      How Many Genes are Needed for a Discriminant Microarray Data Analysis

  7. Machine Learning Techniques

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      Chapter

      Pages 151-165

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

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      Chapter

      Pages 167-182

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

  8. Back Matter

    Pages 183-189