链接预测与连续时间的古典和量子步行
Mark Goldsmith1,2, Harto Saarinen1,2, Guillermo García-Pérez1,2,3,4
1Algorithmiq Ltd., Kanavakatu 3 C, FI-00160 Helsinki, Finland.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
本研究引入了使用经典和量子步行来识别缺失的蛋白质-蛋白质相互作用 (PPI) 的新型链路预测方法. 这些方法有效地预测不完整的生物网络中的相互作用,推进了网络医学.
科学领域:
- 计算生物学 计算生物学
- 网络医学 网络医学
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白与蛋白相互作用 (PPI) 网络对于理解生物系统和网络医学至关重要.
- 目前用于构建PPI网络的方法昂贵,耗时,容易出现不准确性,导致网络数据不完整.
- 推断缺失的相互作用对于全面了解生物网络至关重要.
研究的目的:
- 开发和评估新的链接预测方法,以推断缺失的蛋白质-蛋白质相互作用.
- 探索连续时间的经典和量子步行的应用,以完成PPI网络.
- 评估在量子步行动力学中使用网络邻和拉普拉斯矩阵的有效性.
主要方法:
- 提出了一种基于连续时间的经典随机步行和量子步行的新类链接预测算法.
- 研究了使用网络邻和拉普拉斯矩阵的量子步行.
- 定义了一个基于从步行动态得出的过渡概率的评分函数.
- 在六个现实世界的蛋白质-蛋白质相互作用数据集上测试了这些方法.
主要成果:
- 连续时间的经典随机步行证明了在预测缺失的PPI方面的有效性.
- 使用网络邻矩阵的量子步行也在识别缺失的相互作用方面表现强.
- 提出的方法实现了与最先进的链接预测技术相当的性能.
结论:
- 连续时间的经典和量子步行是推断缺失的蛋白质-蛋白质相互作用的有希望的方法.
- 在量子步行中使用网络邻矩阵对于PPI网络中的链接预测特别有效.
- 这些发现有助于为网络医学应用提供更完整,更准确的生物网络构建.
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