Related: using low "biological resolution" H&E to predict high resolution spatial transcriptomics (2 of 20,000 gene predictions shown)

Across many labs, SCALING single cell foundation models has had mixed success. We think the key is CONTEXT. *Spatial* single cell RNA data preserves the natural biological context of gene expression within animal tissue — in our case, tissue from human patients. When we train models on large, diverse spatial datasets (100M cells across a dozen cancer types) we see BIG benefits from bigger models and longer context (effectively how much patient data the models see at once.) Interestingly, the bigger the model, the better it gets with longer context. Maybe only larger models can capture complex spatial gene expression patterns across large regions of tissue. We think that scaling SPATIAL single cell models is the way — maybe the only way — to discover new, therapeutically actionable biology across patients and solve the CLINICAL TRANSLATION problem that plagues drug development.

Big day for @NOETIK_ai ! We're licensing our OCTO-VirtualCell models in lung and colorectal cancer to @GSK, one of pharma's best at using AI to help patients. @Ronalfa is not being licensed at this time.