scDrugMap:用于药物反应预测的大型基础模型的基准测试
Qing Wang1, Yining Pan1, Minghao Zhou1
1Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.
Nature communications
|December 11, 2025
概括
scDrugMap基准基础模型用于单细胞药物反应预测. scFoundation,UCE和scGPT在不同的环境中表现出最佳表现,推动了精确瘤学的发展.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 耐药性是癌症治疗中的一个重要障碍.
- 单细胞分析揭示了耐药性机制,但对药物反应预测的基础模型尚未得到充分探索.
研究的目的:
- 引入scDrugMap,一个使用单细胞基础模型进行基准测试和预测药物反应的框架.
- 系统地评估各种基础模型在单细胞药物反应预测中的表现.
主要方法:
- 评估了八种单细胞基础模型和两种大型语言模型.
- 分析了来自60个不同数据集的495,000个细胞,涵盖各种组织,药物和癌症类型.
- 在聚合数据,交叉数据 (微调) 和零射击设置中评估模型性能.
主要成果:
- scFoundation表现出强的性能,特别是在瘤组织中.
- 在交叉数据分析中,UCE在微调后取得了最佳结果.
- scGPT在零射击预测中表现出最高的准确性.
结论:
- scDrugMap提供了用于预测单细胞药物反应的基础模型的第一个全面基准.
- 该平台加速药物发现,并通过提供用户友好的工具来支持翻译精确瘤学.
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