We present scBridge, a sophisticated framework for bidirectional cross-modal translation between scRNA-seq and scDNAm profiles with broad biological applicability. scBridge adopts a dual-channel variational autoencoder (VAE) architecture to project scRNA-seq and scDNAm data into a unified latent space, enabling effective cross-modal alignment and translation. To adaptively capture the context-dependent DNA methylation patterns related to gene regulation, we introduce Mixture-of-Experts (MoE) mechanism, which introduces a gating network to dynamically assign input cells to specialized expert subnetworks. .
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