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基于图神经网络模型的系统毒理学研究展望

花匠小妙招
2025-05-15 17:10

摘要:系统毒理学是建立在系统生物学基础上，综合多组学分析和传统毒理学方法，借助生物信息学和计算毒理学等模型化信息整合技术，对生物系统在外源化学物质扰动下保持稳定的能力进行评估，研究外源化学物质与生物系统相互作用机制的一门学科。转录组、蛋白质组、代谢组、暴露组等多组学数据，有多维度、多尺度、多关联的特征，为系统毒理学建模奠定了数据基础。如何利用计算建模，对多组学数据进行有效挖掘成为有待攻克的瓶颈。针对多组学数据的特点，基于网络的模型有着通用性强、灵活性强、包含节点间关系信息等优势，在系统毒理学中起到整合与挖掘多组学数据的关键作用。图神经网络(GNN)作为一种深度学习方法，在系统毒理学建模中展现了良好的应用前景。本文介绍了系统毒理学的研究目的、网络分析方法的研究策略，对GNN在系统毒理学领域的应用进行了展望。

Abstract:Systems toxicology can be regarded as an interdiscipline that is based on systems biology and combines multi-omics data analysis and classical toxicology methods. With information integration technologies such as bioinformatics and computational toxicology, systems toxicology can evaluate ability of biological systems to maintain stability under perturbation of xenobiotics and describe interactions of xenobiotics with biological systems. Transcriptomics, proteomics, metabolomics and exposomics data are multi-dimensional, multi-scaled and multi-correlational, and can lay a data foundation for systems toxicology modeling. It has become a technological bottleneck to use computational modeling for effective mining the multi-omics data. Network-based models exhibit advantages of high generality and flexibility, can characterize relationship information between nodes, and thus are suitable for dealing with multi-omics data, and can play a key role in integrating and mining multi-omics data in systems toxicology. Graph neural network (GNN) as a deep learning method, shows good application prospects in systems toxicology modeling. In this perspective, research orientations of systems toxicology and research strategies of network analysis methods are introduced, and application of GNN in systems toxicology is envisaged.

图 1 基于图神经网络的系统毒理学

Figure 1. Systems toxicology based on graph neural network

图 2 多组学网络模型示意图

Figure 2. Schematic diagram of multi-omics network model

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