Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data.

ABSTRACT: Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time mode

SUBMITTER: Maity AK 

PROVIDER: S-EPMC7007312 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature