\u003cp\u003e\u003ci\u003eIntegration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome \u003c/i\u003ehelps 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.\u003c/p\u003e\u003cul\u003e \u003cli\u003eProvides well described techniques for the purpose of gene selection/feature selection for the generation of gene subsets\u003c/li\u003e \u003cli\u003ePresents 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)\u003c/li\u003e \u003cli\u003eConsolidates fundamental knowledge on gene datasets and current techniques on gene regulatory networks into a single resource\u003c/li\u003e\u003c/ul\u003e
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