Cellina uses supervised disentanglement to separate cell intrinsic states from spatial contexts for counterfactual predictions on tissue graphs, outperforming baselines on 2.5M+ cells from cancer and brain data.
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2026 2verdicts
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K-nearest neighbor from a knowledge graph beats most methods on out-of-distribution transcriptomic perturbation prediction, and an RL-trained reasoning LLM matches SOTA on Replogle et al. (2022) cell lines while improving downstream differential expression prediction.
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Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement
Cellina uses supervised disentanglement to separate cell intrinsic states from spatial contexts for counterfactual predictions on tissue graphs, outperforming baselines on 2.5M+ cells from cancer and brain data.
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Knowledge Graphs and Reasoning LLMs for Finding Simple Yet Effective Transcriptomic Perturbation Predictors
K-nearest neighbor from a knowledge graph beats most methods on out-of-distribution transcriptomic perturbation prediction, and an RL-trained reasoning LLM matches SOTA on Replogle et al. (2022) cell lines while improving downstream differential expression prediction.