Big Data Analytics in Oncology with R
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A01=Atanu Bhattacharjee
advanced R programming
Author_Atanu Bhattacharjee
Bayesian
Bayesian State Space
biostatistics methods
Brier Score
Cart Analysis
Category=PBT
Category=PS
Category=UB
Category=UY
clinical genomics
Competing Risk Scenario
Correlated Frailty Models
Cox Model
Cox PH
Cox PH Model
Cox Proportional Hazard Model
CPH
Data
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eq_computing
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eq_isMigrated=2
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Expression Values
Frailty Distribution
Frailty Model
Gene
Hazard Function
High Dimensional Data
High Dimensional Gene Expression Data
high dimensional survival data analysis
Increased Fold Change
Kaplan Meier Estimator
Lasso
omics data analysis
Oncology
Optimal Threshold Levels
Prognostic Biomarker
Protein Expression Values
Roc Curve
statistical genetics
Survival Analysis
Survival Duration
time-to-event modeling
Product details
- ISBN 9781032028767
- Weight: 512g
- Dimensions: 156 x 234mm
- Publication Date: 29 Dec 2022
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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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.
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.
