The identification of essential proteins in protein-protein interaction (PPI) networks is of great significance for understanding cellular processes. With the increasing availability of large-scale PPI data, numerous centrality measures based on network topology have been proposed to detect essential proteins from PPI networks. However, most of the current approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology annotation information. In this paper, we propose a novel centrality measure, called TEO, for identifying essential proteins by combining network topology, gene expression profiles, and GO information. To evaluate the performance of the TEO method, we compare it with five other methods (degree, betweenness, NC, Pec, and CowEWC) in detecting essential proteins from two different yeast PPI datasets. The simulation results show that adding GO information can effectively improve the predicted precision and that our method outperforms the others in predicting essential proteins.

译文

:蛋白质-蛋白质相互作用(PPI)网络中必需蛋白质的鉴定对理解细胞过程具有重要意义。随着大规模PPI数据可用性的提高,已经提出了许多基于网络拓扑的集中度检测方法来检测PPI网络中的必需蛋白。但是,当前大多数方法主要集中在PPI网络的拓扑结构上,而在很大程度上忽略了基因本体注释信息。在本文中,我们提出了一种新的集中度度量,称为TEO,它通过结合网络拓扑,基因表达谱和GO信息来鉴定必需蛋白质。为了评估TEO方法的性能,我们将其与其他五种方法(度,中间性,NC,Pec和CowEWC)进行比较,以检测来自两个不同酵母PPI数据集的必需蛋白质。仿真结果表明,添加GO信息可以有效地提高预测精度,并且我们的方法在预测必需蛋白质方面优于其他方法。

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