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
Debahuti Mishra & Shruti Mishra
Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome [EPUB ebook]
Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome [EPUB ebook]
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Idioma Inglés ● Formato EPUB ● ISBN 9780128163573 ● Editorial Elsevier Science ● Publicado 2018 ● Descargable 3 veces ● Divisa EUR ● ID 5812774 ● Protección de copia Adobe DRM
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