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A density-based approach for detecting complexes in weighted PPI networks by semantic similarity

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  • HongFang Zhou
  • Jie Liu
  • JunHuai Li
  • WenCong Duan

Abstract

Protein complex detection in PPI networks plays an important role in analyzing biological processes. A new algorithm-DBGPWN-is proposed for predicting complexes in PPI networks. Firstly, a method based on gene ontology is used to measure semantic similarities between interacted proteins, and the similarity values are used as their weights. Then, a density-based graph partitioning algorithm is developed to find clusters in the weighted PPI networks, and the identified ones are considered to be dense and similar. Experimental results demonstrate that our approach achieves good performance as compared with such algorithms as MCL, CMC, MCODE, RNSC, CORE, ClusterOne and FGN.

Suggested Citation

  • HongFang Zhou & Jie Liu & JunHuai Li & WenCong Duan, 2017. "A density-based approach for detecting complexes in weighted PPI networks by semantic similarity," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-14, July.
    • Handle: RePEc:plo:pone00:0180570
    DOI: 10.1371/journal.pone.0180570
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