基于代谢网络的图形神经网络的应用,用于识别毒剂诱导的干扰
Keji Yuan1, Rance Nault1,2
1Department of Pharmacology and Toxicology, Michigan State University, East Lansing, MI 48824, United States.
概括
图形神经网络 (GNN) 揭示了环境毒素隐藏的代谢干扰. 这种人工智能方法揭示了对有毒物质诱导的代谢干扰的新见解,推动了环境健康研究.
科学领域:
- 环境健康 环境健康
- 计算生物学 计算生物学
- 毒理学 毒理学 毒理学
背景情况:
- 转录组分析对于理解生物对环境污染物的反应至关重要.
- 传统的途径分析与代谢网络的复杂性作斗争.
- 代谢网络可以有效地以图形形式表示和分析.
研究的目的:
- 应用基于网络的图形神经网络 (GNN) 来发现由有毒物质引起的新型代谢干扰.
- 研究GNN在识别复杂代谢网络中隐藏的干扰方面的实用性.
- 用人工智能方法来描述生物对有毒物质暴露的反应.
主要方法:
- 在使用Reactome途径对来自26个组织的7,689只小鼠转录组样本进行了GNN模型的训练和验证.
- 应用集成梯度和中心性分析以确定关键反应和途径.
- 利用公开可用的转录组数据,这些数据来自暴露于2,3,7,8-四二子-p-二氧化物 (TCDD) 的小鼠.
主要成果:
- 与对照组相比,在识别TCDD引起的干扰方面实现了100%的性能.
- 在SUMOylation,细胞循环,P53信号和原生物合成途径中发现了干扰.
- 发现了传统路径分析未能揭示的新机理性见解.
结论:
- 图形神经网络提供了一种强大的策略,用于揭示对有毒物质介导的代谢干扰的新型机制性见解.
- GNN可以有效地描述对有毒物质暴露的生物反应,克服传统方法的局限性.
- 这项研究强调了人工智能,特别是GNN在环境健康研究中的潜力,用于发现有毒物质诱导的代谢干扰.
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