多重网络比对中新的基于GO的度量及其统计显著性
New GO-based Measures and Their Statistical Significance in Multiple Network Alignment
AI总结：
本文提出平方GO得分和暴露G得分两种新度量，并引入统计暴露G得分的$p$值度量，用于基于GO术语评估多重PPI网络比对质量及其统计显著性，为基因功能预测和物种进化关系识别提供更可靠工具。
摘要：
蛋白质-蛋白质相互作用（PPI）网络为理解生物系统的功能提供了宝贵见解，比对多个PPI网络可以揭示不同物种之间的重要功能关系。然而，评估多重网络比对的质量是一个具有挑战性的问题。在本文中，我们提出了两种新的度量，即平方GO得分（SGS）和暴露G得分，用于在利用基因本体（GO）术语中的功能信息的同时评估多重网络比对的质量。我们还引入了一个$p$值度量，即统计暴露G得分，用于基于多重网络比对所揭示的GO术语来计算其精确显著性。我们还表明，我们的度量与恢复的直系同源物计数高度相关，为其有效性提供了进一步证据。我们的工作有助于开发更可靠、更准确的度量来评估多重网络比对，并在利用多重网络比对预测基因功能和识别不同物种之间的进化关系方面具有潜在应用。
英文摘要：
Protein-protein interaction (PPI) networks provide valuable insights into the function of biological systems, and aligning multiple PPI networks can reveal important functional relationships between different species. However, assessing the quality of multiple network alignments is a challenging problem. In this paper, we propose two new measures, the Squared GO Score (SGS) and the Exposed G Score, to evaluate the quality of multiple network alignments while using functional information from Gene Ontology (GO) terms. We also introduce a $p$-value measure, the Statistical Exposed G Score, to compute the exact significance of a multiple network alignment based on its revealed GO terms. We also show that our measures are highly correlated with the recovered Ortholog count, providing further evidence for their effectiveness. Our work contributes to the development of more reliable and accurate measures for evaluating multiple network alignments and has potential applications in predicting gene function and identifying evolutionary relationships between different species using multiple network alignment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
