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基因调节网络的推理基于指向图卷积网络

Pi-Jing Wei¹, Ziqiang Guo², Zhen Gao²

¹Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, 230601, Anhui, China.

Briefings in bioinformatics

|June 27, 2024

概括

我们开发了一种基于导向图形卷积神经网络的新方法,用于基因调节网络 (GRN) 推断 (DGCGRN). DGCGRN显著提高了合成和真实生物数据集的预测准确性.

科学领域:

系统生物学系统生物学
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

推断基因调节网络 (GRNs) 是系统生物学中的一个关键挑战.
现有的计算方法面临局限性,特别是与现实世界生物数据集.

研究的目的:

提出一种新的计算方法,以定向图形卷积神经网络为基础的GRN推理方法 (DGCGRN),以改进GRN推理.
提高GRN推断的准确性和稳定性,特别是对复杂的真实生物数据.

主要方法:

利用定向图卷积神经网络 (DGCNN) 来处理GRN结构数据并利用邻近节点的功能.
在图形神经网络中实施了局部增强策略,以解决低度节点的问题.
集成的Bi-GRU用于提取真实数据集 (如E.coli) 的基因序列隐藏特征和计算的物理化学特征.
在培训期间采用动态更新策略,将边缘预测得分转换为边缘权重,用于模型指导.

主要成果:

与合成基准数据集上的现有模型相比,DGCGRN的预测性能明显优越.
该模型还显示了对真实生物数据集的增强性能.
涉及膀尿表皮癌和肺癌细胞的案例研究证实了该模型的有效性.

结论:

DGCGRN为基因调节网络推断提供了一种强大而准确的方法.
该方法有效地处理真实生物数据的复杂性,并改进了现有的技术.
这些发现对理解基因调节在各种生物环境中的影响,包括癌症研究.

关键词:

定向图的卷积网络是指向图的卷积网络.动态更新战略动态更新战略基因监管网络基因监管网络当地的增强战略.序列特征序列特征序列特征

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