基于 GO 的多种网络对齐新方法。
New GO-based measures in multiple network alignment.
机构信息
Department of Computer Science, University of California, Irvine, CA 92697-3435, United States.
Department of Computer Engineering, Sharif University of Technology, Tehran 1458889694, Iran.
出版信息
Bioinformatics. 2024 Aug 2;40(8). doi: 10.1093/bioinformatics/btae476.
MOTIVATION
Protein-protein interaction (PPI) networks provide valuable insights into the function of biological systems. Aligning multiple PPI networks may expose relationships beyond those observable by pairwise comparisons. However, assessing the biological quality of multiple network alignments is a challenging problem.
RESULTS
We propose two new measures to evaluate the quality of multiple network alignments using functional information from Gene Ontology (GO) terms. When aligning multiple real PPI networks across species, we observe that both measures are highly correlated with objective quality indicators, such as common orthologs. Additionally, our measures strongly correlate with an alignment's ability to predict novel GO annotations, which is a unique advantage over existing GO-based measures.
AVAILABILITY AND IMPLEMENTATION
The scripts and the links to the raw and alignment data can be accessed at https://github.com/kimiayazdani/GO_Measures.git.
动机
蛋白质-蛋白质相互作用（PPI）网络为了解生物系统的功能提供了有价值的见解。对齐多个 PPI 网络可以揭示出通过两两比较观察不到的关系。然而，评估多个网络对齐的生物学质量是一个具有挑战性的问题。
结果
我们提出了两种新的度量标准，用于使用来自基因本体论（GO）术语的功能信息评估多个网络对齐的质量。当在跨物种的多个真实 PPI 网络上进行对齐时，我们观察到这两个度量标准都与客观质量指标高度相关，例如常见的直系同源物。此外，我们的度量标准与对齐预测新 GO 注释的能力强烈相关，这是与现有基于 GO 的度量标准相比的独特优势。
可用性和实现
脚本以及原始和对齐数据的链接可在 https://github.com/kimiayazdani/GO_Measures.git 上访问。
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