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.

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.

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.