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A Practical Approach to Microarray Data Analysis

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Cover of 'A Practical Approach to Microarray Data Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Microarray Data Analysis
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    Chapter 2 Data Pre-Processing Issues in Microarray Analysis
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    Chapter 3 Missing Value Estimation
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    Chapter 4 Normalization
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    Chapter 5 Singular Value Decomposition and Principal Component Analysis
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    Chapter 6 Feature Selection in Microarray Analysis
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    Chapter 7 Introduction to Classification in Microarray Experiments
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    Chapter 8 Bayesian Network Classifiers for Gene Expression Analysis
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    Chapter 9 Classifying Microarray Data Using Support Vector Machines
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    Chapter 10 Weighted Flexible Compound Covariate Method for Classifying Microarray Data
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    Chapter 11 Classification of Expression Patterns Using Artificial Neural Networks
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    Chapter 12 Gene Selection and Sample Classification Using a Genetic Algorithm and k-Nearest Neighbor Method
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    Chapter 13 Clustering Genomic Expression Data: Design and Evaluation Principles
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    Chapter 14 Clustering or Automatic Class Discovery: Hierarchical Methods
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    Chapter 15 Discovering Genomic Expression Patterns with Self-Organizing Neural Networks
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    Chapter 16 Clustering or Automatic Class Discovery: Non-Hierarchical, non-SOM
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    Chapter 17 Correlation and Association Analysis
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    Chapter 18 Global Functional Profiling of Gene Expression Data
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    Chapter 19 Microarray Software Review
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    Chapter 20 Microarray Analysis as a Process
Attention for Chapter 12: Gene Selection and Sample Classification Using a Genetic Algorithm and k-Nearest Neighbor Method
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