Tutorial on survival modeling with applications to omics data

We present a general workflow for survival analysis that is applicable to high-dimensional omics data as inputs when identifying survival-associated features and validating survival models. In particular, we focus on the commonly used Cox-type penalized regressions and hierarchical Bayesian models for feature selection in survival analysis, which are are especially useful for high-dimensional data, but the framework is applicable more generally.AVAILABILITY AND IMPLEMENTATION: A step-by-step R tutorial using The Cancer Genome Atlas survival and omics data for the execution and evaluation of survival models has been made available at https://ocbe-uio.github.io/survomics/survomics.html.PMID:38445722 | DOI:10.1093/bioinformatics/btae132
Source: Genomics Proteomics ... - Category: Genetics & Stem Cells Authors: Source Type: research