SENA-discrepancy-VAE combines a pathway-masked encoder with discrepancy-VAE, giving causal latent factors interpretable as biological process combinations with comparable unseen-perturbation prediction.
URL https://dl.acm.org/doi/10.5555/ 3495724.3495797
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Interpretable Causal Representation Learning for Biological Data in the Pathway Space
SENA-discrepancy-VAE combines a pathway-masked encoder with discrepancy-VAE, giving causal latent factors interpretable as biological process combinations with comparable unseen-perturbation prediction.