Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India.
Department of Computer and System Sciences, Visva-Bharati, Santiniketan 731235, West Bengal, India.
J Bioinform Comput Biol. 2020 Aug;18(4):2050025. doi: 10.1142/S0219720020500250. Epub 2020 Aug 6.
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.
蛋白质复合物是大多数生物过程的基石。从蛋白质-蛋白质相互作用 (PPI) 数据 (等效地，从 PPI 网络) 中识别蛋白质复合物对于理解细胞组织的原理非常重要，其具有包括疾病诊断在内的几个重要应用。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，从 PPI 网络）中识别蛋白质复合物。这些 PPI 数据存在大量的假阳性，这是正确识别蛋白质复合物的一个瓶颈。基于基因本体论 (GO) 的语义相似性度量可以用于为 PPIs 分配置信分数。因此，低置信度的 PPIs 很可能是假阳性。在本文中，我们使用基于 GO 的语义相似性度量系统地研究了低置信度 PPIs 对基于 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个最先进的复杂检测算法和九个基于 GO 的相似性度量。我们发现，在过滤低相似度评分的 PPIs 后，每个复杂检测算法的性能都显著提高。还观察到，百分比提高和过滤百分比（低置信度 PPIs）高度相关。
