DAGFormer:基于图形的域适应方法用于预测单细胞癌症药物反应
Fen Yan1, ZhiHua Du1, Yu-An Huang2,3
1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
PLoS computational biology
|December 19, 2025
概括
DAGFormer集成了批量和单细胞RNA测序数据,以预测癌症药物反应,改善对瘤异质性和耐药机制的理解. 这种新的基于图形的域调整框架提高了个性化癌症治疗预测的准确性.
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.
- 癌症研究 癌症研究
背景情况:
- 准确的单细胞药物反应预测对于理解瘤异质性和改善癌症治疗至关重要.
- 目前集成批量和单细胞RNA测序 (scRNA-seq) 数据的方法面临着批量效应和有限吞吐量等挑战.
- 现有的方法往往忽略了细胞间相互作用,将细胞视为孤立的实体.
研究的目的:
- 开发一个新的计算框架,DAGFormer,用于强大的单细胞药物反应预测.
- 为了有效地整合批量和scRNA-seq数据,克服现有方法的局限性.
- 提高对瘤异质性和癌症耐药性机制的理解.
主要方法:
- DAGFormer使用基于图的域调整 (GDA) 框架来集成批量和scRNA-seq数据.
- 细胞邻近图是使用多种拓策略构建的.
- 双域解码器解了共享和模式特定的表示,以实现可靠的知识传输.
主要成果:
- 与现有方法相比,DAGFormer在预测单细胞药物反应方面表现优异.
- 该框架有效地解决了批量效应和批量和scRNA-seq数据之间的分布差距.
- 在十个独立的scRNA-seq数据集上的基准测试证实了DAGFormer的有效性和稳定性.
结论:
- DAGFormer通过整合多模式测序数据,提供了一种强大的方法来预测单细胞药物反应.
- 该框架增强了对瘤异质性和癌症细胞间相互作用的理解.
- DAGFormer在推进个性化癌症治疗和药物发现方面具有重大潜力.
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