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Activity Number: 172
Type: Contributed
Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #320449 View Presentation
Title: A Bayesian Approach for the Integrative Analysis of Omics Data
Author(s): Thierry Chekouo Tekougang* and Francesco Stingo and Kim-Anh Do and James Doecke
Companies: MD Anderson Cancer Center and MD Anderson Cancer Center and MD Anderson Cancer Center and CSIRO Health and Biosecurity
Keywords: Bayesian variable selection ; Integrating multi-regressions ; Markov random field ; Non-local prior

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. Moreover, interactions between platforms can be incorporated through prior distributions to inform the selection of biomarkers and additionally improve biomarker selection accuracy. 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.

Authors who are presenting talks have a * after their name.

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