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.
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1. Literature Review2. SVM-BT-RFE: An Improved Gene Selection Framework Using Bayesian T-Test Embedded in Support Vector Machine (Recursive Feature Elimination) Algorithm3. Enhanced Gene Ranking Approaches Using Modified Trace Ratio Algorithm for Gene Expression Data4. SNR-TR Gene Ranking Method: A Signal-to-Noise Ratio Based Gene Selection Algorithm Using Trace Ratio for Gene Expression Data5. Visualization of Interactive Gene Regulatory Network Using Gene Selection Techniques from Expression Data6. Conclusion and Future Work
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Presents best practices in gene selection and gene regulatory networks
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
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Produktdetaljer
ISBN
9780128163566
Publisert
2018-05-11
Utgiver
Vendor
Academic Press Inc
Vekt
290 gr
Høyde
235 mm
Bredde
191 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
200
Forfatter