Big Data Analytics in Oncology with R

Big Data Analytics in Oncology with R provides a comprehensive text to work with recent development in the area, covering gene expression data analysis, survival analysis, and Bayesian methods. It is aimed at graduates and researchers studying survival analysis or statistical methods in genetics.

Format: Hardback
Length: 254 pages
Publication date: 29 December 2022
Publisher: Taylor & Francis Ltd

Big Data Analytics in Oncology with R is a powerful tool for analyzing large datasets in the field of oncology. With the advancements in advanced computation using R, there has been significant progress in handling big data. However, working with big data presents several technical challenges that need to be addressed. These challenges revolve around computational aspects and finding the fastest way to obtain computational results.

In today's cutting-edge oncology research, clinical decision-making based on genomic information and survival outcomes has become indispensable. This book aims to provide a comprehensive text that covers various recent developments in the area of big data analytics in oncology.

The book begins by discussing gene expression data analysis using R, a powerful tool for analyzing biological data. It covers topics such as analyzing microarray data, identifying differentially expressed genes, and conducting survival analysis using R. The book also includes a section on survival analysis using R, which is a crucial tool for predicting patient outcomes and analyzing survival data.

One of the key features of this book is its discussion of bayesian in survival-gene expression analysis. Bayesian methods are increasingly being used in survival analysis, as they provide a flexible and robust approach to modeling and analyzing data. The book covers various topics related to bayesian in survival-gene expression analysis, including parameter estimation, model selection, and validation.

Another important aspect of big data analytics in oncology is competing-gene expression analysis. This analysis involves identifying genes that are differentially expressed between two or more groups of patients and analyzing their association with survival outcomes. The book discusses competing-gene expression analysis using R, including methods such as logistic regression, Cox regression, and survival analysis with random effects.

Furthermore, the book covers Bayesian on survival with omics data, which is a growing area of research in oncology. Omics data refers to the analysis of large datasets that include various types of biological information, such as gene expression, proteomics, and metabolomics. Bayesian on survival with omics data involves integrating these different types of data to better understand the underlying biological mechanisms and predict patient outcomes.

This book is aimed primarily at graduates and researchers studying survival analysis or statistical methods in genetics. It provides a comprehensive and up-to-date introduction to big data analytics in oncology, covering both theoretical concepts and practical applications. The book is well-written, easy to understand, and filled with examples and case studies that illustrate the key concepts and techniques discussed.

In conclusion, Big Data Analytics in Oncology with R is a valuable resource for anyone interested in analyzing large datasets in the field of oncology. With its comprehensive coverage of gene expression data analysis, survival analysis, bayesian in survival-gene expression analysis, competing-gene expression analysis, and Bayesian on survival with omics data, the book provides a solid foundation for understanding and applying big data analytics in this area. Whether you are a graduate student, researcher, or healthcare professional, this book will help you gain insights into the latest developments in big data analytics and its applications in oncology.

Weight: 532g
Dimension: 162 x 240 x 21 (mm)
ISBN-13: 9781032028767