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用BioPathNet对生物医学知识图的基于路径推理

Yue Hu^1,2, Svitlana Oleshko^1,3, Samuele Firmani¹

¹Computational Health Center, Helmholtz Center Munich, Ingolstaedter Landstrasse 1, Neuherberg, 85764, Bavaria, Germany.

bioRxiv : the preprint server for biology

|August 16, 2024

概括

生物PathNet是一个新的图形神经网络,通过考虑路径,而不仅仅是节点来增强生物医学链接预测. 这种可解释的框架提高了药物发现和个性化医学的准确性.

科学领域:

生物医学信息学是生物医学信息学.
网络生物学网络生物学
机器学习在医疗保健中的应用

背景情况:

生物医学网络分析对于进步至关重要,但传统的链接预测方法缺乏复杂性和可解释性.
代表性学习提高了准确性,但面临着可扩展性和可解释性挑战.

研究的目的:

介绍BioPathNet,一个新的图形神经网络框架用于生物医学链接预测.
通过基于路径的推理,提高生物医学知识图的准确性和可解释性.

主要方法:

在链接预测中,BioPathNet利用神经贝尔曼-福特网络 (NBFNet) 进行基于路径的推理.
它包含一个背景监管图 (BRG),用于改进消息传递和严格的负采样.
通过考虑路径上的所有关系来学习节点对之间的表示.

主要成果:

在基因功能注释,药物疾病指示,合成致死率和lncRNA-mRNA相互作用预测方面,BioPathNet的表现优于现有的方法.
预测的新药适用于ALL和阿尔茨海默病,由专家和临床试验验证.
通过文献审查确认了新的合成致死性基因对和调节性相互作用.

结论:

在生物医学链接预测任务中,BioPathNet提供了卓越的性能和多功能性.
它的可解释性使研究人员能够追踪分子洞察的预测路径.
生物路径网是加速药物发现和个性化医学的宝贵工具.

关键词:

生物医学知识图表图表神经网络的神经网络链接预测链接预测

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