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

HongFang Zhou; Jie Liu; JunHuai Li; WenCong Duan

doi:10.1371/journal.pone.0180570

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

PLoS One. 2017 Jul 12;12(7):e0180570. doi: 10.1371/journal.pone.0180570. eCollection 2017.

Authors

HongFang Zhou¹, Jie Liu¹, JunHuai Li¹, WenCong Duan¹

Affiliation

¹ School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China.

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.

MeSH terms

Algorithms
Protein Interaction Mapping / methods*
Protein Interaction Maps
Semantics*

Grants and funding

This research is supported by the National Natural Science Foundation of China (61402363), Education Department of Shaanxi Province Key Laboratory Project (15JS079), Xi’an Science Program Project (CXY1509(7)), and Beilin district of Xi’an Science and Technology Project (GX1625).