Generalizing perturbation effects across unseen perturbations, contexts and datasets
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Generalizing perturbation effects across unseen perturbations, contexts and datasets
Stack is a single-cell foundation model that enables in-context learning at inference time.
State is a multi-scale AI model that operates on sets of cells to capture perturbation effects using a modern transformer architecture.
Evo 2 is a genomic foundation model capable of generalist prediction and design tasks across DNA, RNA, and proteins.