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Membrane lipids such as phosphatidylinositol (PI) are precursors for several membrane-bound and soluble second messengers. Specific kinases phosphorylate PI and produce phosphorylated inositol phospholipids. One such inositol phospholipids are the  phosphatidylinositol-4,5 bisphosphate [PI(4,5)P2], present in the inner half of the lipid bilayer. Upon ligand binding, GPCR stimulates Gq proteins to turn on phospholipase Cꞵ. Activated phospholipase Cꞵ cleaves PI(4,5)P2 and...
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用BioPathNet对生物医学知识图的基于路径推理.

Yue Hu1,2, Svitlana Oleshko1,3, Samuele Firmani1

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

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PubMed
概括
此摘要是机器生成的。

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.

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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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.