Protein complexes are the cornerstones of most of the biological processes. Identifying protein complexes is crucial in understanding the principles of cellular organization with several important applications, including in disease diagnosis. Several computational techniques have been developed to identify protein complexes from protein–protein interaction (PPI) data (equivalently, from PPI networks). These PPI data have a significant amount of false positives, which is a bottleneck in identifying protein complexes correctly. Gene ontology (GO)-based semantic similarity measures can be used to assign a confidence score to PPIs. Consequently, low-confidence PPIs are highly likely to be false positives. In this paper, we systematically study the impact of low-confidence PPIs on the performance of complex detection methods using GO-based semantic similarity measures. We consider five state-of-the-art complex detection algorithms and nine GO-based similarity measures in the evaluation. We find that each complex detection algorithm significantly improves its performance after the filtration of low-similarity scored PPIs. It is also observed that the percentage improvement and the filtration percentage (of low-confidence PPIs) are highly correlated.

中文翻译：

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低置信度相互作用对蛋白质复合物计算鉴定的影响

蛋白质复合物是大多数生物过程的基石。识别蛋白质复合物对于理解具有多种重要应用（包括疾病诊断）的细胞组织原理至关重要。已经开发了几种计算技术来从蛋白质-蛋白质相互作用 (PPI) 数据（等效地，来自 PPI 网络）中识别蛋白质复合物。这些 PPI 数据有大量的误报，这是正确识别蛋白质复合物的瓶颈。基于基因本体 (GO) 的语义相似性度量可用于为 PPI 分配置信度分数。因此，低置信度 PPI 很可能是误报。在本文中，我们系统地研究了低置信度 PPI 对使用基于 GO 的语义相似性度量的复杂检测方法性能的影响。我们在评估中考虑了五种最先进的复杂检测算法和九种基于 GO 的相似性度量。我们发现，在过滤低相似度得分的 PPI 后，每种复杂的检测算法都显着提高了其性能。还观察到改进百分比和过滤百分比（低置信度 PPI）高度相关。