BayReL: Bayesian Relational Learning for Multi-omics Data Integration
This work addresses the challenge of multi-omics data integration for researchers in genomics and systems biology, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of inferring relational interactions across multi-omics data types by developing BayReL, a Bayesian representation learning method, which demonstrates enhanced performance in inferring meaningful interactions compared to existing baselines on real-world datasets.
High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale. Studying molecular interactions across different data types helps reveal signal transduction mechanisms across different classes of molecules. In this paper, we develop a novel Bayesian representation learning method that infers the relational interactions across multi-omics data types. Our method, Bayesian Relational Learning (BayReL) for multi-omics data integration, takes advantage of a priori known relationships among the same class of molecules, modeled as a graph at each corresponding view, to learn view-specific latent variables as well as a multi-partite graph that encodes the interactions across views. Our experiments on several real-world datasets demonstrate enhanced performance of BayReL in inferring meaningful interactions compared to existing baselines.