基因调控网络的推理基于多视图层次超图
Songyang Wu1, Kui Jin1, Mingjing Tang2,3
1School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China.
Interdisciplinary sciences, computational life sciences
|February 11, 2024
概括
这项研究引入了一种新的多视图层次超图GRN (MHHGRN) 模型,用于高级基因调节网络推断. MHHGRN有效地捕捉复杂的基因相互作用,在精度和跨物种概括方面超过现有方法.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 基因调控网络 (GRN) 的推断对于理解复杂的生物系统至关重要.
- 当前的图形学习方法往往简化了转录因子 (TF) -目标基因相互作用的配对,未能捕捉到更高阶的调节模式.
- 现有的方法也可能忽略基因表达数据中的结构GRN信息,并依赖于有限的先前知识.
研究的目的:
- 开发一种先进的GRN推断模型,能够捕捉许多对许多,高阶监管关系.
- 整合异构的生物信息和结构GRN数据,以提高推断准确度.
- 通过结合信息扩散和跨域消息传递来增强基因嵌入表示.
主要方法:
- 提出了一个多视图层次超图GRN (MHHGRN) 推理模型.
- 构建了多视图层次超图,整合了TF和目标基因的异质生物数据.
- 采用超图形卷积网络,合信息扩散,跨域消息传递,以及适应性特征学习的道注意力机制.
主要成果:
- 与E. coli和S. cerevisiae基准数据集的基线方法相比,MHHGRN表现优越 (DREAM5挑战).
- 该模型表现出极好的跨物种概括能力.
- 在来自多个小鼠和人类细胞系的scRNA-seq数据集上取得了可比或更好的结果.
结论:
- MHHGRN有效地模拟了复杂的,高阶基因调节关系,超出了对对相互作用.
- 拟议的模型在GRN推断准确性和稳定性方面提供了显著的改进.
- MHHGRN显示出各种生物应用的巨大潜力,包括跨物种分析和单细胞RNA测序数据.
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