Fig. 1From: MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoderFramework 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 networksBack to article page