基因调控连接的预测与联合单细胞基础模型和基于图形的学习
Sindhura Kommu1, Yizhi Wang2, Yue Wang2
1Department of Computer Science, Virginia Tech, Blacksburg, 24061, Virginia, USA.
我们开发了scRegNet,这是一个使用单细胞基础模型 (scFMs) 和图形学习预测基因调节网络从scRNA-seq数据的新框架. 这种方法提高了推断基因相互作用的准确性和稳定性.
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广泛的转录因子-DNA结合数据,这些数据通常是有限的和昂贵的.
研究的目的:
- 使用scRNA-seq数据开发一个强大的基因调节链路预测框架.
- 利用大规模预训练的单细胞基础模型 (scFMs) 和基于图形的联合学习来克服现有方法的局限性.
主要方法:
- 提出了scRegNet,这是一个新的框架,将scFMs与基于图形的联合学习相结合,用于基因调节链接的预测.
- 利用矢量化基因层次表示来预测缺失的调节相互作用.
- 评估了七个scRNA-seq基准数据集的性能,与九种基线方法相比.
主要成果:
- scRegNet取得了最先进的结果,在七个基准数据集上表现优于九种基准方法.
- 拟议的框架在噪音数据上训练时,与基线方法相比,表现出更高的稳定性.
结论:
- scRegNet提供了一种强大而有效的方法,可以从scRNA-seq数据中准确地推断基因调节网络.
- scFMs和图形学习的整合为推进GRN推理方法提供了一个有希望的方向.
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