
Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome
Description
Key Features
- Provides well described techniques for the purpose of gene selection/feature selection for the generation of gene subsets
- Presents and analyzes three different types of gene selection algorithms: Support Vector Machine-Bayesian T-Test-Recursive Feature Elimination (SVM-BT-RFE), Canonical Correlation Analysis-Trace Ratio (CCA-TR), and Signal-To-Noise Ratio-Trace Ratio (SNRTR)
- Consolidates fundamental knowledge on gene datasets and current techniques on gene regulatory networks into a single resource
Readership
Bioinformaticians, Cancer Researchers, researchers interested in applying Systems Biology approaches to their studies; Geneticists, Bioengineers, researchers interested in Machine learning, Data Mining, Bioinformatics
Table of Contents
1. Literature Review
2. SVM-BT-RFE: An Improved Gene Selection Framework Using Bayesian T-Test Embedded in Support Vector Machine (Recursive Feature Elimination) Algorithm
3. Enhanced Gene Ranking Approaches Using Modified Trace Ratio Algorithm for Gene Expression Data
4. SNR-TR Gene Ranking Method: A Signal-to-Noise Ratio Based Gene Selection Algorithm Using Trace Ratio for Gene Expression Data
5. Visualization of Interactive Gene Regulatory Network Using Gene Selection Techniques from Expression Data
6. Conclusion and Future Work
Product details
- No. of pages: 200
- Language: English
- Copyright: © Academic Press 2018
- Published: May 9, 2018
- Imprint: Academic Press
- Paperback ISBN: 9780128163566
- eBook ISBN: 9780128163573
About the Authors
Shruti Mishra
Affiliations and Expertise
Debahuti Mishra
Affiliations and Expertise
Sandeep Satpathy
Affiliations and Expertise
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