Bay ReL: Bayesian Relational Learning for Multi-omics Data Integration: Supplementary Materials

To further clarify the model and workflow of our proposed BayReL, we provide a schematic illustration of BayReL in Figure S1, where we only include two views for clarity. Figure S2 shows the inferred bipartite network with the top 200 interactions by BayReL. Schematic illustration of BayReL. 2 Figure S2: The bipartite sub-network with the top 200 interactions inferred by BayReL in AML data, Genes and drugs are shown as blue and red nodes, respectively. D. Details on the experimental setups, hyper-parameter selection, and run time We learn the model for 1000 training epochs and use the validation set for early stopping. Each training epoch for CF, BRCA, and AML took 0.01, 0.42, In all experiments, we used CCAGFA R package as the official implementation of BCCA.