Large Language Models Meet Virtual Cell: A Survey
Krinos Li, Xianglu Xiao, Shenglong Deng, Lucas He, Zijun Zhong, Yuanjie Zou, Zhonghao Zhan, Zheng Hui, Weiye Bao, Guang Yang
大型语言模型(LLMs)正在通过开发“虚拟细胞”来改变细胞生物学 - 计算系统,这些系统代表,预测和推理细胞状态和行为。 这项工作提供了用于虚拟细胞建模的LLM的全面审查。 我们提出了一个统一的分类法,将现有方法组织成两个范式:LLM作为Oracle,用于直接蜂窝建模,LLM作为代理,用于编排复杂的科学任务。 我们确定了三个核心任务 - 细胞表征,扰动预测和基因调控推理 - 并审查其相关模型,数据集,评估基准以及可扩展性,可推广性和可解释性方面的关键挑战。
Large language models (LLMs) are transforming cellular biology by enabling the development of "virtual cells"–computational systems that represent, predict, and reason about cellular states and behaviors. This work provides a comprehensive review of LLMs for virtual cell modeling. We propose a unified taxonomy that organizes existing methods into two paradigms: LLMs as Oracles, for direct cellular modeling, and LLMs as Agents, for orchestrating complex scientific tasks. We identify three core ta...