生物启发的图形神经网络编码了反应体,并揭示了疾病的生化反应
Joshua G Burkhart1, Guanming Wu2, Xubo Song3
1Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, HI 96813, USA.
Patterns (New York, N.Y.)
|July 31, 2023
概括
一个新的图形神经网络 (GNN) 有效地分析复杂的人体组织数据,识别精准医学的分子标. 这种方法在揭示疾病机制方面超越了传统方法,例如牛皮.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 人体组织研究因功能异质性而面临挑战,阻碍了精确的治疗标识.
- 现有的分析方法可能无法完全捕捉组织内的复杂分子相互作用.
研究的目的:
- 开发和验证基于Reactome架构的图形神经网络 (GNN),用于分析人类组织分子数据.
- 评估GNN与传统模型的性能及其识别疾病特异性分子扰动的能力.
主要方法:
- 在9,115个基因型-组织表达 (GTEx) 样本上训练了一个GNN,使用基于Reactome的架构.
- 在癌症基因组图谱 (TCGA) 数据集上使用调整的兰德指数 (ARI) 评估了GNN性能.
- 应用了GNN来区分健康的皮肤和病变性牛皮样本.
主要成果:
- 与具有显著更少参数的Resnet18模型 (0.7781) 相比,GNN实现了更高的ARI (0.7909).
- 成功地将健康的皮肤与牛皮样本区分开来,确定了牛皮中涉及的关键途径.
- 揭示了26S和NUB1介导的NEDD8,UBD和合物降解的上调,作为牛皮病理学的核心.
结论:
- 开发的GNN提供了一种强大而高效的方法来分析复杂的人体组织数据,其性能优于传统模型.
- 这种基于GNN的策略可以揭示分子疾病机制,如牛皮,这些机制被传统分析所遗漏.
- 这些发现表明,GNN对未来的分子疾病研究和精确治疗开发具有重大前景.
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