# Conditional Variational Autoencoder

Bulk mucosal RNA sequencing in inflammatory bowel disease has been analyzed one cohort at a time, so historical data accumulate without becoming a reusable reference. IBDex trained a tissue-aware conditional variational autoencoder on 3,168 biopsies from 1,680 patients across four cohorts and suppressed cohort identity adversarially, then froze the gene interface, encoder, decoder, pathway basis and phenotype directions. After freezing, dataset identity fell to balanced accuracy 0.307 against chance 0.250 while inflammation stayed recoverable (AUC 0.82); in 93 patients with concordant endoscopic and histological remission, inflammation approached controls (AUC 0.58) but disease state stayed displaced (AUC 0.725).

Bulk mucosal RNA sequencing in inflammatory bowel disease has been analyzed one cohort at a time, so historical data accumulate without becoming a reusable reference. IBDex trained a tissue-aware conditional variational autoencoder on 3,168 biopsies from 1,680 patients across four cohorts and suppressed cohort identity adversarially, then froze the gene interface, encoder, decoder, pathway basis and phenotype directions. After freezing, dataset identity fell to balanced accuracy 0.307 against chance 0.250 while inflammation stayed recoverable (AUC 0.82); in 93 patients with concordant endoscopic and histological remission, inflammation approached controls (AUC 0.58) but disease state stayed displaced (AUC 0.725).

Co2ODE models only the part of a biochemical network that carries signal in a given cellular context: a conditional mask selects the markers tied to rewiring, and a variational autoencoder compresses the rest into a learned closure term trained jointly with a sparse ODE. On single-cell phosphoproteomic data spanning 30 cancer-relevant overexpressions, it compresses a 30-marker panel into a 10-dimensional state while matching the target-trajectory accuracy of a neural ODE fit to the full panel; adding a prior score shifts the selected markers at almost no cost in fit, indicating these data do not identify the true rewiring.

Co2ODE models only the part of a biochemical network that carries signal in a given cellular context: a conditional mask selects the markers tied to rewiring, and a variational autoencoder compresses the rest into a learned closure term trained jointly with a sparse ODE. On single-cell phosphoproteomic data spanning 30 cancer-relevant overexpressions, it compresses a 30-marker panel into a 10-dimensional state while matching the target-trajectory accuracy of a neural ODE fit to the full panel; adding a prior score shifts the selected markers at almost no cost in fit, indicating these data do not identify the true rewiring.