Premium
A Bayesian integrative approach for multi‐platform genomic data: A kidney cancer case study
Author(s) -
Chekouo Thierry,
Stingo Francesco C.,
Doecke James D.,
Do KimAnh
Publication year - 2017
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12587
Subject(s) - bayesian probability , kidney cancer , computer science , cancer , computational biology , data mining , artificial intelligence , medicine , biology
Summary 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.