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.
Moreover, the number of genes that are differentially expressed following a perturbation is generally lower in the Wessels2023 study than in Norman2019 (Fig
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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.