Excited to share our new paper on Contextual AI models for context-specific prediction in biology in @NatureMethods led by stellar @_michellemli rdcu.be/dOxQ7 Understanding how proteins work and developing new therapies requires knowing which cell types proteins act in and how they interact with each other. Predicting and optimizing protein targets must happen in context. We can draw a parallel with the polysemic word “apple,” whose meaning is resolved via the context of surrounding words. Just as one can “grow an apple” or “buy an apple,” the roles of genes and proteins are resolved via cell context—particularly the environment where a drug will operate. In this @naturemethods paper, we introduce PINNACLE, an approach that uses geometric deep learning to create context-aware protein models. Using a protein interaction dataset and a multi-organ single-cell atlas @cziscience, PINNACLE analyzes protein interactions in 156 cell types across 24 tissues, generating nearly 395,000 protein representations. PINNACLE dynamically adjusts its outputs to biological contexts. Excited to soon share with you models across @cziscience #CellxGene Discover datasets. It paves the way for a type of AI that can learn contexts to understand a given protein plus its surrounding environment and identify its many potential roles. Providing outputs tailored to biological contexts is essential for the broad use of foundation models in biology. We tested PINNACLE on tasks such as enhancing 3D structural representations of therapeutically relevant interactions in immunology, studying the effects of drugs across cell-type contexts, nominating therapeutic targets in a cell-type-specific manner, and zero-shot retrieval of tissue hierarchy. @harvard @HarvardDBMI @harvard_data @KempnerInst @Roche @MassGenBrigham @BrighamWomens Fantastic team of collaborators: @_michellemli, M Sumathipala, MQ Liang, A Valdeolivas, AN Ananthakrishnan @kat_liao @danmarbach Thanks to @DrArunimaSingh for editorial guidance Paper: nature.com/articles/s41592-0… Research Briefing: nature.com/articles/s41592-0… Code: github.com/mims-harvard/PINN… HF Space: huggingface.co/spaces/michel…