Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome

Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome helps readers identify and select the specific genes causing oncogenes. The book also addresses the validation of the selected genes using various classification techniques and performance metrics, making it a valuable source for cancer researchers, bioinformaticians, and researchers from diverse fields interested in applying systems biology approaches to their studies.

• 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

Bioinformaticians, Cancer Researchers, researchers interested in applying Systems Biology approaches to their studies; Geneticists, Bioengineers, researchers interested in Machine learning, Data Mining, Bioinformatics

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