期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 19, 期 5, 页码 2882-2893出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2021.3095947
关键词
Proteins; Semantics; Clustering algorithms; Task analysis; Topology; Ontologies; Search problems; Protein complex identification; fuzzy clustering; protein-protein interaction network; gene ontology
类别
资金
- Natural Science Foundation of Xinjiang Uygur Autonomous Region [2021D01D05]
- Pioneer Hundred Talents Program of Chinese Academy of Sciences
- NSFC Excellent Young Scholars Program [61722212]
Protein complexes play a crucial role in understanding protein biological processes. In this study, we propose a novel fuzzy-based clustering framework called FCAN-PCI, which considers both network topology and protein attribute information to improve identification accuracy and identify overlapping complexes.
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.
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