Abstract
Proteins usually bind together to form complexes, which play an important role in cellular activities. Many graph clustering methods have been proposed to identify protein complexes by finding dense regions in protein-protein interaction networks. We present a novel framework (CPL) that detects protein complexes by propagating labels through interactions in a network, in which labels denote complex identifiers. With proper propagation in CPL, proteins in the same complex will be assigned with the same labels. CPL does not make any strong assumptions about the topological structures of the complexes, as in previous methods. The CPL algorithm is tested on several publicly available yeast protein-protein interaction networks and compared with several state-of-the-art methods. The results suggest that CPL performs better than the existing methods. An analysis of the functional homogeneity based on a gene ontology analysis shows that the detected complexes of CPL are highly biologically relevant.
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The work was supported by the National Natural Science Foundation of China under Grant Nos. 61271346, 61172098, and 91335112, the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20112302110040, and the Fundamental Research Funds for the Central Universities of China under Grant No. HIT.KISTP.201418.
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Dai, QG., Guo, MZ., Liu, XY. et al. CPL: Detecting Protein Complexes by Propagating Labels on Protein-Protein Interaction Network. J. Comput. Sci. Technol. 29, 1083–1093 (2014). https://doi.org/10.1007/s11390-014-1492-z
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DOI: https://doi.org/10.1007/s11390-014-1492-z