Mining Overlapping Protein Complexes in PPI Network Based on Granular Computation in Quotient Space
Proteins complexes play a critical role in many biological processes. The existing protein complex detection algorithms are mostly cannot reflect the overlapping protein complexes. In this paper, a novel algorithm is proposed to detect overlapping protein complexes based on granular computation in quotient space. Firstly, problems are expressed by quotient space and different quotient space embodies the quotient set of different granular. Then the method estimates the relationship between particles to make up for the inadequacy of data in combination with the PPI data and Gene Ontology data, deals with the network based on quotient space theory. Graining the network to construct the quotient space and merging the particles layer by layer. The final protein complexes is obtained after purification. The experimental results on Saccharomyces cerevisiae and Homo sapiens turned out that the proposed method could exploit protein complexes more accurately and efficiently.
KeywordsProtein complexes Gene Ontology Quotient space Granular computation Clustering
This paper is supported by the National Natural Science Foundation of China (61672334, 61502290, 61401263) and the Fundamental Research Funds for the Central Universities, Shaanxi Normal University (GK201804006).
- 7.Min, W., Li, X., Kwoh, C.K., Ng, S.K.: A core-attachment based method to detect protein complexes in PPI networks. BMC Bioinf. 10, 1–16 (2009)Google Scholar
- 8.Van Dongen, S.: Graph clustering by flow simulation. Ph.D. thesis University of Utrecht (2000)Google Scholar
- 12.Xu, F., Zhang, L., Wang, L.: Approach of the fuzzy granular computing based on the theory of quotient space. Pattern Recognit. Artif. Intell. 17, 424–429 (2004)Google Scholar
- 15.Zhao, S., Wang, K.E., Chen, J., et al.: Community detection algorithm based on clustering granulation. J. Comput. Appl. 34, 2812–2815 (2014)Google Scholar
- 19.Tang, Y., Min, L.I.: A cytoscape plugin for visualization and clustering analysis of protein interaction networks. Chin. J. Bioinf. (2014)Google Scholar