揭示隐藏的监管依赖关系:为单细胞基因监管网络推断推断多视角图形学习
Wenying He1, Rentao Zhang1, Yaowei Zhu1
1School of Artificial Intelligence, Hebei University of Technology, No. 5340, Xiping Road, Beichen District, Tianjin, 300400, China.
Briefings in bioinformatics
|January 19, 2026
概括
这项研究介绍了ATFGRN,这是一种用于推断基因调节网络 (GRNs) 的新型图形神经框架. ATFGRN通过整合结构,表达和相似性特征来提高准确性,改善基因调节关系预测.
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:
- 基因调节网络 (GRNs) 对于理解细胞过程和疾病至关重要.
- 从单细胞转录组数据中准确推断GRN是具有挑战性的,因为数据的复杂性.
- 整合多层次的基因特征是改善GRN推断的关键.
研究的目的:
- 开发一个先进的框架,ATFGRN,用于准确的基因调节关系预测.
- 有效地整合多样化的基因特征,以增强GRN推断.
- 从单细胞数据改进基因调控网络构建的准确性.
主要方法:
- ATFGRN使用了一个自适应拓特征融合图形神经框架.
- 它包含子图结构编码,表达式引导图卷积网络与自我注意,以及KNN图与图注意相似性.
- 这些模块的功能通过基于注意力的权重机制被融合在一起.
主要成果:
- 在预测基因调节关系方面,ATFGRN表现得更好.
- 对单细胞转录基因数据集的评估显示,与现有方法相比,AUROC的性能增加了5.09%.
- 多视角的核聚变战略被证明是有效和适用的.
结论:
- ATFGRN为准确的GRN推断提供了一种强大的方法.
- 整合结构,表达和相似性视角可以增强预测能力.
- 这一框架推进了使用单细胞转录组数据对基因调节的分析.
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