The spike-and-slab lasso Cox model for survival prediction and associated genes detection.

ABSTRACT:

Motivation

Large-scale molecular profiling data have offered extraordinary opportunities to improve survival prediction of cancers and other diseases and to detect disease associated genes. However, there are considerable challenges in analyzing large-scale molecular data.

Results

We propose new Bayesian hierarchical Cox proportional hazards models, called the spike-and-slab lasso Cox, for predicting survival outcomes and detecting associated genes. We also develop an efficient algorithm to fit the proposed models by incorporating Expectation-Maximization steps into the extremely fast cyclic coordinate descent algorithm. The performance of the proposed method is assessed via extensive simulations and compared with the lasso Cox regression. We demonstrate the proposed procedu

SUBMITTER: Tang Z 

PROVIDER: S-EPMC5870779 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature