scOTM:使用大型语言模型预测单细胞扰乱反应的深度学习框架
Yuchen Wang1, Tianchi Lu1, Xingjian Chen2
1Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR 999077, China.
Bioengineering (Basel, Switzerland)
|August 28, 2025
概括
我们开发了scOTM, 一种深度学习模型, 这种方法克服了现有方法的局限性,通过灵活地建模转录转移并将其推广到新的细胞类型.
科学领域:
- 计算生物学
- 基因组学
- 机器学习
背景情况:
- 对单细胞药物反应的准确建模对于医疗保健至关重要.
- 目前的方法与未配对的数据扎, 缺乏生物解释性.
- 现有的模型往往需要限制性预先对齐,从而限制它们的表达力.
研究的目的:
- 开发一个深度学习框架,scOTM,用于从未配对的数据中预测单细胞扰动反应.
- 提高对未见细胞类型的概括性,并提高生物解释性.
- 克服现有方法在处理未配对数据和严格的先前约束方面的局限性.
主要方法:
- scOTM将来自大型语言模型的生物知识集成到一个变化自编码器中.
- 最大平均差异规范化允许转录转移的灵活建模.
- 最佳运输可以在控制和扰乱细胞分布之间建立可解释的映射.
主要成果:
- 在预测全转录组反应和识别差异表达基因方面,scOTM 的性能优于现有的方法.
- 在数据有限的场景中,该框架表现出卓越的稳定性.
- scOTM 在多种细胞类型中表现出强大的泛化能力.
结论:
- scOTM为预测单细胞药物反应提供了强大而灵活的框架.
- 该方法通过提供可解释的嵌入和灵活的建模来增强生物学理解.
- scOTM通过有效处理未配对的数据和对新细胞类型的概括来推进该领域.
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