A0806
Title: Multi-omics data integration and interpretation
Authors: Himel Mallick - Cornell University (United States)
Anupreet Porwal - University of Washington (United States)
Satabdi Saha - University of Texas MD Anderson Cancer Center (United States)
Piyali Basak - Merck & Co., Inc (United States) [presenting]
Vladimir Svetnik - Merck (United States)
Erina Paul - Merck (United States)
Abstract: With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction. A novel Bayesian ensemble method is proposed to consolidate prediction by combining information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, the approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries.
