Big Data Analytics in Oncology with R
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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.
- 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.
Table of Contents
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
Atanu Bhattacharjee is working as Lecturer in Medical Statistics at the University of Leicester, United Kingdom. He previously served as an Assistant Professor at the Section of Biostatistics, Centre for Cancer Epidemiology, Tata Memorial Centre, India, and the Malabar Cancer Centre, Kerala, India. He completed his Ph.D. at Gauhati University, Assam, on Bayesian Statistical Inference. He is an elected member of the International Biometric Society (Indian Region). He has published over 250 research articles in various peer-reviewed journals.