Big Data Analytics in Oncology with R
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.

1141870521
Big Data Analytics in Oncology with R
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.

190.0 In Stock
Big Data Analytics in Oncology with R

Big Data Analytics in Oncology with R

by Atanu Bhattacharjee
Big Data Analytics in Oncology with R
Add to Wishlist
Big Data Analytics in Oncology with R

Big Data Analytics in Oncology with R

by Atanu Bhattacharjee

Overview

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.

Product Details

ISBN-13: 9781032028767
Publisher: CRC Press
Publication date: 12/29/2022
Pages: 270
Product dimensions: 6.12(w) x 9.19(h) x (d)

About the Author

Atanu Bhattacharjee worked as an Assistant Professor at the Section of Biostatistics, Centre for Cancer Epidemiology, Tata Memorial Centre, India. He previously taught Biostatistics at the Malabar Cancer Centre, Kerala, India. He completed his PhD 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.

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
From the B&N Reads Blog
Page 1 of

Related Subjects

Science & Technology
Mathematics
Probability Theory
Science & Technology
Mathematics
Probability Theory

Customer Reviews