Closing the loop: Teaching single-cell foundation models to learn from perturbations.

ABSTRACT: The application of transfer learning models to large scale single-cell datasets has enabled the development of single-cell foundation models (scFMs) that can predict cellular responses to perturbations in silico. Although these predictions can be experimentally tested, current scFMs are unable to "close the loop" and learn from these experiments to create better predictions. Here, we introduce a "closed-loop" framework that extends the scFM by incorporating perturbation data during model fine-tuning. Our closed-loop model improves prediction accuracy, increasing positive predictive value in the setting of T-cell activation three-fold. We applied this model to RUNX1-familial platelet disorder, a rare pediatric blood disorder and identified two therapeutic targets (mTOR and CD74-MIF signalin

SUBMITTER: Pershad Y 

PROVIDER: S-EPMC12265564 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature