Big Data Analytics in Oncology with R - Atanu Bhattacharjee

Big Data Analytics in Oncology with R

Buch | Hardcover
254 Seiten
2022
Chapman & Hall/CRC (Verlag)
978-1-032-02876-7 (ISBN)
168,35 inkl. MwSt
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.
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.

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.

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

Erscheinungsdatum
Zusatzinfo 131 Tables, black and white; 28 Line drawings, black and white; 28 Illustrations, black and white
Sprache englisch
Maße 156 x 234 mm
Gewicht 540 g
Themenwelt Mathematik / Informatik Informatik Theorie / Studium
Mathematik / Informatik Mathematik
Naturwissenschaften Biologie
ISBN-10 1-032-02876-9 / 1032028769
ISBN-13 978-1-032-02876-7 / 9781032028767
Zustand Neuware
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