Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.
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 RIncludes bayesian in survival-gene expression analysisDiscusses competing-gene expression analysis using RCovers Bayesian on survival with omics dataThis book is aimed primarily at graduates and researchers studying survival analysis or statistical methods in genetics.
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
Velmurugan D, VELMURUGAN D, D Velmurugan, Atanu Bhattacharjee, D Gayathri, India) Velmurugan, D (Srm Univ, India) Bhattacharjee, Atanu (North Eastern Hill Univ, India) Gayathri, D (Univ Of Madras
Velmurugan D, VELMURUGAN D, D Velmurugan, Atanu Bhattacharjee, D Gayathri, India) Velmurugan, D (Srm Univ, India) Bhattacharjee, Atanu (North Eastern Hill Univ, India) Gayathri, D (Univ Of Madras