用联合单细胞基础模型和基于图形的学习模型预测基因调节连接
Sindhura Kommu1, Yizhi Wang2, Yue Wang2
1Department of Computer Science, Virginia Tech, Blacksburg, VA 24060, United States.
Bioinformatics (Oxford, England)
|July 15, 2025
概括
我们开发了scRegNet,这是一个新的框架,使用单细胞基础模型和图形学习来从scRNA-seq数据中预测基因调节网络. 它的性能优于现有的方法,即使有噪音数据.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 单细胞RNA测序 (scRNA-seq) 能够进行详细的基因调控网络 (GRN) 推断,但由于数据稀疏和噪声而面临挑战.
- 对GRN推断的监督机器学习方法需要大量的转录因子-DNA结合数据,这些数据通常是有限的和昂贵的.
- 大规模的预培训和转移学习提供了一个有希望的方法来克服GRN推断中的数据限制.
研究的目的:
- 通过利用大规模预训练模型来解决当前GRN推断方法的局限性.
- 开发一个强大的基因调节链路预测框架,使用矢量化基因表示.
- 为了提高GRN从杂的scRNA-seq数据推断的准确性和可靠性.
主要方法:
- 使用在广泛的scRNA-seq数据集上训练的单细胞基础模型 (scFMs).
- 实施基于图形的联合学习,以整合scFMs,以进行强大的基因调控链接预测.
- 将GRN推断作为基因调节链路预测任务,使用基因级向量化表示.
主要成果:
- 在七个基准scRNA-seq数据集中,scRegNet实现了最先进的性能.
- 拟议的框架在基因调控链接预测方面表现优于九种基线方法.
- 在对噪音数据进行训练时,scRegNet与基线方法相比表现出更高的稳定性.
结论:
- scRegNet为准确的GRN推断提供了一个新且有效的框架.
- 利用scFMs和基于图形的学习显著改善了基因调节链接的预测.
- 开发的方法为分析复杂的scRNA-seq数据和推断基因调节相互作用提供了强大的解决方案.
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