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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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.
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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

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Product details

ISBN
9781032028767
Published
2022-12-29
Publisher
Taylor & Francis Ltd
Weight
512 gr
Height
234 mm
Width
156 mm
Age
U, 05
Language
Product language
Engelsk
Format
Product format
Innbundet
Number of pages
254