GeOKG:用于基因本体学和基因的几何意识知识图嵌入
Chang-Uk Jeong1,2,3, Jaesik Kim3,4, Dokyoon Kim2,3
1Department of Software and Computer Engineering, Ajou University, Suwon, 16499, South Korea.
Bioinformatics (Oxford, England)
|April 11, 2025
概括
几何意识知识图嵌入 (GeOKG) 使用几何交互来建模基因本体学 (GO) 层次结构. 这种方法通过更好地捕捉复杂的生物关系来改善蛋白质-蛋白质相互作用的预测.
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
- 网络科学 网络科学
背景情况:
- 对于基因本体学 (GO) 和基因本体学注释 (GOA) 的深度学习,表示学习有助于预测蛋白质-蛋白质相互作用等生物任务.
- 现有的方法将GO和GOA嵌入单个几何空间中,这对于GO的复杂,非单调的层次结构是不够的.
研究的目的:
- 为了解决模拟GO的等级结构中的局限性.
- 开发一种新的方法来增强GO和GOA的代表性学习.
主要方法:
- 拟议的几何意识知识图嵌入 (GeOKG) 方法.
- 在训练期间利用各种几何表示之间的几何相互作用.
- 更有效地模拟了GO的复杂层次结构.
主要成果:
- GeOKG有效地模拟了GO的复杂等级结构.
- 在政府级别的实验证明了几何相互作用的好处.
- 在基因层面上,GeOKG在蛋白质-蛋白质相互作用预测方面表现优于现有的方法.
结论:
- 几何交互是嵌入异质生物医学网络的一个有希望的方法.
- GeOKG增强了GO和GOA对下游生物任务的表示学习.
- 这些发现突出了改善生物网络分析的新方向.
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