Unified methods for feature selection in large-scale genomic studies with censored survival outcomes.

ABSTRACT:

Motivation

One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes which provide insight into the disease process. With rapid developments in high-throughput genomic technologies in the past two decades, the scientific community is able to monitor the expression levels of tens of thousands of genes and proteins resulting in enormous datasets where the number of genomic features is far greater than the number of subjects. Methods based on univariate Cox regression are often used to select genomic features related to survival outcome; however, the Cox model assumes proportional hazards (PH), which is unlikely to hold for each feature. When applied to genomic features exhibiting some form of non-proportional hazards

SUBMITTER: Spirko-Burns L 

PROVIDER: S-EPMC7267818 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature