The spike-and-slab lasso Cox model for survival prediction and associated genes detection.
Bioinformatics
; 33(18): 2799-2807, 2017 Sep 15.
Article
en En
| MEDLINE
| ID: mdl-28472220
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 procedure on two cancer datasets with censored survival outcomes and thousands of molecular features. Our analyses suggest that the proposed procedure can generate powerful prognostic models for predicting cancer survival and can detect associated genes. AVAILABILITY AND IMPLEMENTATION The methods have been implemented in a freely available R package BhGLM ( http//www.ssg.uab.edu/bhglm/ ). CONTACT nyi@uab.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Modelos de Riesgos Proporcionales
/
Biología Computacional
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2017
Tipo del documento:
Article