B1919
Title: An integrated Bayesian framework for multi-omics prediction and classification
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: A novel Bayesian ensemble method to consolidate prediction by combining information across several longitudinal and cross-sectional omics data layers is proposed. Unlike existing frequentist paradigms, this approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. The method is applied to four published multi-omics datasets and it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification.
