scGSDR:利用基因语义来进行单细胞药理分析
Yu-An Huang1,2, Xiyue Cao2, Zhu-Hong You3
1Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China.
Communications biology
|October 30, 2025
概括
我们开发了scGSDR,这是一个计算模型,通过整合基因语义和通路来预测细胞对药物的反应. 该工具通过识别药物耐药性机制并帮助开发向治疗来增强精准医学.
科学领域:
- 计算生物学 计算生物学
- 药物基因组学 药物基因组学
- 精准医学是一门精准的医学.
背景情况:
- 单细胞测序揭示了细胞异质性在抗药性中的作用.
- 计算模型可以使用现有数据预测细胞对药物的反应.
- 了解基因语义和信号通路对于生物学见解至关重要.
研究的目的:
- 开发一种计算模型 (scGSDR),用于预测细胞对药物的反应.
- 通过结合基因语义和通路信息来提高预测性能.
- 确定关键的途径和基因,有助于药物耐药性表型.
主要方法:
- 整合了两个计算管道,重点关注细胞状态和基因信号通路.
- 将基因语义纳入模型,以提高预测准确度.
- 开发了一个可解释性模块,以识别与耐药性相关的途径和基因.
主要成果:
- 与使用大量或单细胞RNA测序数据的现有方法相比,scGSDR显示出更高的预测准确性.
- 该模型成功地预测了对单一药物和药物组合的反应.
- 注意力得分的生物解释性确定了相关的基因 (例如,BCL2,CCND1,PIK3CA) 和参与药物耐药性的途径.
结论:
- scGSDR通过结合基因语义,有效地模拟细胞对各种药物的反应,包括组合.
- 该模型有助于识别关键的耐药性途径,推进精准医学.
- scGSDR通过精确定位潜在的药物相关基因,促进了针对性治疗的开发.
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