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Identifying Protein Complexes From Protein-Protein Interaction Networks Based on Fuzzy Clustering and GO Semantic Information
IEEE/ACM Transactions on Computational Biology and Bioinformatics ( IF 4.5 ) Pub Date : 2021-07-09 , DOI: 10.1109/tcbb.2021.3095947
Xiangyu Pan 1 , Lun Hu 2 , Pengwei Hu 2 , Zhu-Hong You 3
Affiliation  

Protein complexes are of great significance to provide valuable insights into the mechanisms of biological processes of proteins. A variety of computational algorithms have thus been proposed to identify protein complexes in a protein-protein interaction network. However, few of them can perform their tasks by taking into account both network topology and protein attribute information in a unified fuzzy-based clustering framework. Since proteins in the same complex are similar in terms of their attribute information and the consideration of fuzzy clustering can also make it possible for us to identify overlapping complexes, we target to propose such a novel fuzzy-based clustering framework, namely FCAN-PCI, for an improved identification accuracy. To do so, the semantic similarity between the attribute information of proteins is calculated and we then integrate it into a well-established fuzzy clustering model together with the network topology. After that, a momentum method is adopted to accelerate the clustering procedure. FCAN-PCI finally applies a heuristical search strategy to identify overlapping protein complexes. A series of extensive experiments have been conducted to evaluate the performance of FCAN-PCI by comparing it with state-of-the-art identification algorithms and the results demonstrate the promising performance of FCAN-PCI.

中文翻译:

基于模糊聚类和 GO 语义信息从蛋白质-蛋白质相互作用网络中识别蛋白质复合物

蛋白质复合物对于提供有价值的蛋白质生物学过程机制的见解具有重要意义。因此,已经提出了多种计算算法来识别蛋白质-蛋白质相互作用网络中的蛋白质复合物。然而,很少有人可以通过在统一的基于模糊的聚类框架中同时考虑网络拓扑和蛋白质属性信息来执行任务。由于同一复合物中的蛋白质在属性信息方面是相似的,并且模糊聚类的考虑也可以使我们识别重叠的复合物,我们的目标是提出这样一种新颖的基于模糊的聚类框架,即 FCAN-PCI,以提高识别精度。为此,计算蛋白质属性信息之间的语义相似度,然后将其与网络拓扑一起集成到一个完善的模糊聚类模型中。之后,采用动量法来加速聚类过程。FCAN-PCI 最终应用启发式搜索策略来识别重叠的蛋白质复合物。已经进行了一系列广泛的实验,通过将 FCAN-PCI 与最先进的识别算法进行比较来评估 FCAN-PCI 的性能,结果证明了 FCAN-PCI 的良好性能。FCAN-PCI 最终应用启发式搜索策略来识别重叠的蛋白质复合物。已经进行了一系列广泛的实验,通过将 FCAN-PCI 与最先进的识别算法进行比较来评估 FCAN-PCI 的性能,结果证明了 FCAN-PCI 的良好性能。FCAN-PCI 最终应用启发式搜索策略来识别重叠的蛋白质复合物。已经进行了一系列广泛的实验,通过将 FCAN-PCI 与最先进的识别算法进行比较来评估 FCAN-PCI 的性能,结果证明了 FCAN-PCI 的良好性能。
更新日期:2021-07-09
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