An integrated Bayesian framework for multi-omics prediction and classification

Abstract

With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to the more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype prediction from cross-sectional, single-omics data modalities but decentralized frameworks that jointly analyze multiple time-dependent omics data to highlight the integrative and dynamic impact of repeatedly measured biomarkers are currently limited. In this article, we propose a novel Bayesian ensemble method to consolidate prediction by borrowing information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, our approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. We apply our method to four published multi-omics datasets and demonstrate that it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification. Our open-source software is publicly available at https://github.com/himelmallick/IntegratedLearner

Integrating omics atlas in health informatics system design-an opinion article

“…As an example, “IntegratedLearner” ( 22 ) is a recent integrated model using a fully Bayesian Ensemble approach for classification and prediction through a multi-layer omics dataset controlling for single-layer omics bias. “IntegratedLearner” uses two-stage feature selection, allowing adjustment for confounding (e.g., environment, lifestyle) effects in both cross-sectional and longitudinal data.…”