大型语言模型用于翻译性癌症信息学
Yining Pan1, Yanfei Wang1, Guangyu Wang2,3
1Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL.
JCO clinical cancer informatics
|October 14, 2025
概括
大型语言模型 (LLM) 为分析复杂的癌症数据提供了强大的工具,包括单细胞,空间转录和病理信息. 将这些模型定制为癌症研究将加速疾病机制和治疗策略的发现.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 癌症是全球主要的死亡原因.
- 高通量转录和病理学数据带来了分析挑战.
- 大型语言模型 (LLM) 对生物医学数据分析有希望.
研究的目的:
- 审查LLM在癌症研究中的应用.
- 探索LLM在细胞和病理癌症数据分析中的应用.
- 突出LLM在推进精密瘤学的潜力.
主要方法:
- 对当前的LLM (2022-2025) 进行系统审查.
- 专注于单细胞RNA测序,空间转录组学和组织学图像的LLM.
- 强调与癌症生物学和翻译研究的相关性.
主要成果:
- 在单细胞数据中,LLM用于细胞注释,集成和药物反应预测.
- 帮助空间数据集成,归算和细胞通信分析.
- 在病理学中,LLM有助于癌症亚型,突变预测和图像细分.
结论:
- 通过可扩展的数据分析工具,LLM可以显著推进癌症研究.
- 针对特定领域的微调对于优化LLM用于癌症应用至关重要.
- 定制的LLM将增强生物标志物发现和治疗策略开发.
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