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Big Data Analytics in Oncology with R

Autor Atanu Bhattacharjee
en Limba Engleză Hardback – 29 dec 2022
Big Data Analytics in Oncology with R serves the analytical approaches for big data analysis. There is huge progressed in advanced computation with R. But there are several technical challenges faced to work with big data. These challenges are with computational aspect and work with fastest way to get computational results. Clinical decision through genomic information and survival outcomes are now unavoidable in cutting-edge oncology research. This book is intended to provide a comprehensive text to work with some recent development in the area.
Features:
  • Covers gene expression data analysis using R and survival analysis using R
  • Includes bayesian in survival-gene expression analysis
  • Discusses competing-gene expression analysis using R
  • Covers Bayesian on survival with omics data
This book is aimed primarily at graduates and researchers studying survival analysis or statistical methods in genetics.
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Specificații

ISBN-13: 9781032028767
ISBN-10: 1032028769
Pagini: 270
Ilustrații: 131 Tables, black and white; 28 Line drawings, black and white; 28 Illustrations, black and white
Dimensiuni: 156 x 234 x 21 mm
Greutate: 0.54 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC

Public țintă

Postgraduate and Undergraduate Advanced

Cuprins

1. Survival Analysis. 2. Cox Proportional Survival Analysis. 3. Parametric Survival Analysis. 4. Competing Risk Modeling in High Dimensional Data. 5. Biomarker Thresholding in High Dimensional Data. 6. High Dimensional Survival Data Analysis. 7. Frailty Models. 8. Time-Course Gene Expression Data Analysis. 9. Survival Analysis and Time-course Data Analysis. 10. Features Selection in High Dimensional Time to Event Data

Descriere

This book is intended to provide a comprehensive coverage about survival and omics-gene expression data analysis for oncology research and to highlight some recent development in the area. It will guide to perform survival analysis with gene expression data using R & is aimed at researchers studying statistical methods in genetics.