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MK-TGAN outperforms traditional generative models by integrating biological knowledge, producing synthetic transcriptomic data that is both realistic and useful for research.
M$^3$-Gen reveals how multimodal integration can generate interpretable gene expression profiles, bridging the gap between clinical imaging and molecular data.
