A Bayesian integrative approach for multi-platform genomic data: A kidney cancer case study.
Biometrics
; 73(2): 615-624, 2017 06.
Article
em En
| MEDLINE
| ID: mdl-27669160
ABSTRACT
Integration of genomic data from multiple platforms has the capability to increase precision, accuracy, and statistical power in the identification of prognostic biomarkers. A fundamental problem faced in many multi-platform studies is unbalanced sample sizes due to the inability to obtain measurements from all the platforms for all the patients in the study. We have developed a novel Bayesian approach that integrates multi-regression models to identify a small set of biomarkers that can accurately predict time-to-event outcomes. This method fully exploits the amount of available information across platforms and does not exclude any of the subjects from the analysis. Through simulations, we demonstrate the utility of our method and compare its performance to that of methods that do not borrow information across regression models. Motivated by The Cancer Genome Atlas kidney renal cell carcinoma dataset, our methodology provides novel insights missed by non-integrative models.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Renais
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Biometrics
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
Estados Unidos