为下一代单细胞分析扩展大型语言模型
Syed Asad Rizvi1, Daniel Levine2, Aakash Patel2
1Yale University, Google Research.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
我们开发了C2S-Scale,这是一个大型语言模型 (LLM),在超过10亿个转录和生物文本数据标记上进行训练. 这种先进的模型增强了单细胞RNA测序分析,并通过整合各种数据类型来指导生物发现.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 人工智能的人工智能
背景情况:
- 单细胞RNA测序 (scRNA-seq) 显示了细胞多样性,但目前的模型缺乏可扩展性和文本集成性.
- 现有的单细胞基础模型 (scFMs) 难以处理各种任务,并将转录组数据与生物文本结合起来.
- 该Cell2Sentence (C2S) 框架将scRNA-seq配置文件表示为文本,为整合各种数据提供了基础.
研究的目的:
- 通过整合转录和文本数据,开发一个可扩展和多用途的单细胞分析基础模型.
- 通过对大量生物数据进行培训,增强LLM在生物研究方面的能力.
- 证明C2S-Scale模型在预测生物反应和指导实验发现方面的实用性.
主要方法:
- 通过使用C2S框架对超过10亿个转录数据,生物文本和元数据令牌进行了27亿参数的LLM培训.
- 使用强化学习进行有针对性的微调,以提高特定生物任务的性能.
- 利用双语境虚拟屏幕来提名药物候选人,以选择语境生物效应.
主要成果:
- 在各种下游任务中,C2S-Scale在预测和生成能力方面取得了持续的改进.
- 该模型在扰乱响应预测,自然语言解释和生物推理方面取得了强的表现.
- 一个虚拟屏幕提名了silmitasertib (CX-4945) 进行情境选择性抗原呈现上调,这在人类细胞模型中经过实验验证.
- C2S-Scale在前所未有的规模上有效地整合了转录和文本数据,超过了专门的scFM和一般的LLM.
结论:
- C2S-Scale代表了单细胞分析的重大进步,为下一代研究统一了各种数据类型.
- 该模型能够指导环境条件下的生物发现,这为药物开发和生物理解开辟了新的途径.
- C2S-Scale提供了一个强大的平台来创建"虚拟细胞",并推动计算生物学的界限.
相关概念视频
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