ChatCell: Facilitating Single-Cell Analysis with Natural Language
<|reference_start|>ChatCell: Facilitating Single-Cell Analysis with Natural Language: As Large Language Models (LLMs) rapidly evolve, their influence in science is becoming increasingly prominent. The emerging capabilities of LLMs in task generalization and free-form dialogue can significantly advance fields like chemistry and biology. However, the field of single-cell biology, which forms the foundational building blocks of living organisms, still faces several challenges. High knowledge barriers and limited scalability in current methods restrict the full exploitation of LLMs in mastering single-cell data, impeding direct accessibility and rapid iteration. To this end, we introduce ChatCell, which signifies a paradigm shift by facilitating single-cell analysis with natural language. Leveraging vocabulary adaptation and unified sequence generation, ChatCell has acquired profound expertise in single-cell biology and the capability to accommodate a diverse range of analysis tasks. Extensive experiments further demonstrate ChatCell's robust performance and potential to deepen single-cell insights, paving the way for more accessible and intuitive exploration in this pivotal field. Our project homepage is available at https://zjunlp.github.io/project/ChatCell.<|reference_end|>
@article{fang2024chatcell:,
    title={ChatCell: Facilitating Single-Cell Analysis with Natural Language},
    author={Yin Fang, Kangwei Liu, Ningyu Zhang, Xinle Deng, Penghui Yang, Zhuo
  Chen, Xiangru Tang, Mark Gerstein, Xiaohui Fan, Huajun Chen},
    journal={arXiv preprint arXiv:2402.08303},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08303},
    primaryClass={cs.CL cs.AI cs.CE cs.HC cs.LG}
}