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High-throughput molecular profiling technologies have producedhigh-dimensional multi-omics data, enabling systematic understanding of livingsystems at the genome scale. Studying molecular interactions across differentdata types helps reveal signal transduction mechanisms across different classesof molecules. In this paper, we develop a novel Bayesian representationlearning method that infers the relational interactions across multi-omics datatypes. Our method, Bayesian Relational Learning (BayReL) for multi-omics dataintegration, takes advantage of a priori known relationships among the sameclass of molecules, modeled as a graph at each corresponding view, to learnview-specific latent variables as well as a multi-partite graph that encodesthe interactions across views. Our experiments on several real-world datasetsdemonstrate enhanced performance of BayReL in inferring meaningful interactionscompared to existing baselines.

BayReL: Bayesian Relational Learning for Multi-omics Data Integration