通过使用基础模型的转移学习来预测未见的细胞类型的药物反应
Yixuan Wang1, Xinyuan Liu1, Yimin Fan1
1Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China.
Nature computational science
|October 3, 2025
概括
预测新细胞类型的药物反应是很困难的. 我们的新框架CRISP使用人工智能准确预测未见的细胞类型中的药物效应,帮助药物重新定位和开发.
科学领域:
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
- 基因组学就是基因组学.
背景情况:
- 药物重新定位提供了一个具有成本效益的药物开发策略.
- 在疾病进展过程中预测新型细胞类型的药物反应是一个重大挑战.
- 目前的方法缺乏对细胞类型特定预测的概括性.
研究的目的:
- 在单细胞分辨率下开发一个框架,用于预测以前未见的细胞类型中的药物干扰反应.
- 提高细胞类型特定药物反应预测的概括性和准确性.
- 证明该框架对药物重用应用的有用性.
主要方法:
- 介绍了细胞类型特定的药物干扰反应预测器 (CRISP) 框架.
- 杆基础模型和特定于细胞类型的学习策略.
- 在各种场景中评估CRISP,包括未见的细胞类型和跨平台预测.
主要成果:
- 在预测新型细胞类型中药物反应方面,CRISP表现出更好的概括性和性能.
- 该框架有效地将信息从控制转移到扰乱状态,即使数据有限.
- 从固体瘤数据的零射击预测准确地确定了索拉费尼布在慢性髓性白血病中的治疗作用.
结论:
- 克里斯普提供了一种强大的方法,用于预测未经表征的细胞类型中的药物反应.
- 该框架显示了加速药物改用和开发的巨大潜力.
- 预测的抗瘤机制,如CXCR4通路抑制,通过独立研究得到验证,支持慢性髓性白血病的治疗策略.
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