Fig. 1

Framework of the MOCAT. The top panel shows the overall architecture of the proposed model: 1) high-dimensional features of multiple omics datasets are fed into an autoencoder network for dimensionality reduction to obtain representative features; 2) three omics-specific auxiliary classifiers are trained to assist in learning more compact and accurate sub-representations; 3) feature sub-representations are fused and further compressed by an autoencoder network a self-attention module to fuse the complementary information embedded across multi-omics adaptively; 4) confident network (ConfNet) is employed to adjust the prediction confidence linked to the fused features adaptively. The bottom panel illustrates the detailed architectures of the autoencoder, attention, and confidence networks