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Cell2Sentence

Cell2Sentence AI Agent
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Overview

Open-source framework that turns single-cell gene expression into 'cell sentences' so LLMs can analyze and generate biology insights.

Cell2Sentence (C2S) is an open-source framework from the van Dijk Lab that represents single-cell RNA-seq profiles as textual “cell sentences” (genes ordered by expression), enabling large language models to natively model biology. The project includes code, docs, and open models (C2S-Scale) released with Google Research/DeepMind for state-of-the-art single-cell tasks like cell typing, perturbation prediction, and virtual cell generation.

Autonomy level

54%

Reasoning: Cell2Sentence (C2S-Scale) demonstrates intermediate autonomy in biological research applications. The model exhibits significant independent reasoning capabilities: it can autonomously generate novel hypotheses about cancer cell behavior, predict cellular responses to drug treatments and perturbations, identify between 10-30% novel drug candidates ...

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Some of the use cases of Cell2Sentence:

  • Transforming scRNA-seq data into text so LLMs can learn cellular patterns.
  • Training and evaluating LLMs on single-cell tasks (e.g., cell type prediction).
  • Generating “virtual cells” and simulating perturbation responses.
  • Integrating biology text and metadata with transcriptomic signals for joint modeling.
  • Reproducing C2S pipelines with open code, docs, and released models.

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Popularity level: 70%

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