A Practical Approach to Microarray Data Analysis
In the past several years, DNA microarray technology has attracted tremendous interest in both the scientific community and in industry. With its ability to simultaneously measure the activity and interactions of thousands of genes, this modern technology promises unprecedented new insights into mec...
| Other Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer US
2003, 2003
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| Edition: | 1st ed. 2003 |
| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- to Microarray Data Analysis
- Data Pre-Processing Issues in Microarray Analysis
- Missing Value Estimation
- Normalization
- Singular Value Decomposition and Principal Component Analysis
- Feature Selection in Microarray Analysis
- to Classification in Microarray Experiments
- Bayesian Network Classifiers for Gene Expression Analysis
- Classifying Microarray Data Using Support Vector Machines
- Weighted Flexible Compound Covariate Method for Classifying Microarray Data
- Classification of Expression Patterns Using Artificial Neural Networks
- Gene Selection and Sample Classification Using a Genetic Algorithm and k-Nearest Neighbor Method
- Clustering Genomic Expression Data: Design and Evaluation Principles
- Clustering or Automatic Class Discovery: Hierarchical Methods
- Discovering Genomic Expression Patterns with Self-Organizing Neural Networks
- Clustering or Automatic Class Discovery: Non-Hierarchical, non-SOM
- Correlation and Association Analysis
- Global Functional Profiling of Gene Expression Data
- Microarray Software Review
- Microarray Analysis as a Process