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  "documentTitle": "2021 Air Street Capital The State of AI Report 2021",
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      "text": "At evaluation time, we can swap out the model's perturbation embedding to answer the counterfactual question \"What would have the gene expression of this cell looked like, had it been treated differently?\"",
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      "text": "An autoencoder is used to encode and learn embeddings for the transcriptional response of single cells to 30 drug treatments across different cell types, doses, and drug combinations.\nThe model learns three additive embeddings: the cell's basal state, the observed perturbation, and the observed covariates.\nAt evaluation time, we can swap out the model's perturbation embedding to answer the counterfactual question \"What would have the gene expression of this cell looked like, had it been treated differently?\"",
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      "text": "Combination therapy could improve cancer patient outcomes, but empirically testing a large number of them is unfeasible in the lab setting. Here, self-supervision is used to observe cells treated with a finite number of drug combinations and to predict the effect of unseen combinations.",
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      "kind": "title",
      "text": "Introduction | Research | Talent | Industry | Politics | Predictions",
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