使用循环一致性学习预测单细胞细胞对干扰的反应
1College of Computer and Information Engineering, Nanjing Tech University, Nanjing, Jiangsu 211816, China.
Bioinformatics (Oxford, England)
|June 28, 2024
概括
我们开发了cycleCDR,这是一个深度学习框架,用于预测细胞对药物的反应. 这种方法通过学习干扰的可转移表示方式,有效地将其推广到新的药物上.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 药物发现 药物发现 药物发现
背景情况:
- 基于表型的药物查识别了与细胞相互作用的化合物.
- 由于干扰而发生的细胞状态变化通过转录和蛋白质基因分析揭示出来.
研究的目的:
- 提出一个新的深度学习框架 cycleCDR,用于预测细胞对外部干扰的反应.
- 通过学习干扰的可转移表示方式,使得对未见的药物进行概括.
主要方法:
- 使用自动编码器将未被扰乱的细胞状态映射到隐藏空间.
- 实施周期一致性约束,用于隐性空间中的扰动建模.
- 应用线性添加模型用于潜伏空间内的药物扰动效应.
主要成果:
- 循环CDR在现有最先进的方法上表现出卓越的性能.
- 该框架显示了大量和单细胞转录和蛋白质组数据集的多功能性.
- 实验验证证证实了该模型对未见药物的概括能力.
结论:
- 循环CDR提供了一种多功能和有效的深度学习方法,用于预测细胞对干扰的反应.
- 该方法学习可转移表示的能力提高了其在药物发现和系统生物学中的适用性.
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