Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome
Nonfiction, Science & Nature, Science, Biological Sciences, Biology
| Author: | Debahuti Mishra, Sandeep Kumar Satapathy, Shruti Mishra, PhD | ISBN: | 9780128163573 |
| Publisher: | Elsevier Science | Publication: | May 9, 2018 |
| Imprint: | Academic Press | Language: | English |
| Author: | Debahuti Mishra, Sandeep Kumar Satapathy, Shruti Mishra, PhD |
| ISBN: | 9780128163573 |
| Publisher: | Elsevier Science |
| Publication: | May 9, 2018 |
| Imprint: | Academic Press |
| Language: | English |
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
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
