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Molecules 2017, 22(12), 2179; doi:10.3390/molecules22122179

A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks

Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi, China
Author to whom correspondence should be addressed.
Received: 9 November 2017 / Revised: 3 December 2017 / Accepted: 3 December 2017 / Published: 8 December 2017
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Most proteins perform their biological functions while interacting as complexes. The detection of protein complexes is an important task not only for understanding the relationship between functions and structures of biological network, but also for predicting the function of unknown proteins. We present a new nodal metric by integrating its local topological information. The metric reflects its representability in a larger local neighborhood to a cluster of a protein interaction (PPI) network. Based on the metric, we propose a seed-expansion graph clustering algorithm (SEGC) for protein complexes detection in PPI networks. A roulette wheel strategy is used in the selection of the seed to enhance the diversity of clustering. For a candidate node u, we define its closeness to a cluster C, denoted as NC(u, C), by combing the density of a cluster C and the connection between a node u and C. In SEGC, a cluster which initially consists of only a seed node, is extended by adding nodes recursively from its neighbors according to the closeness, until all neighbors fail the process of expansion. We compare the F-measure and accuracy of the proposed SEGC algorithm with other algorithms on Saccharomyces cerevisiae protein interaction networks. The experimental results show that SEGC outperforms other algorithms under full coverage. View Full-Text
Keywords: graph clustering; protein complex detection; seed expansion; protein interaction network graph clustering; protein complex detection; seed expansion; protein interaction network

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, J.; Zheng, W.; Qian, Y.; Liang, J. A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks. Molecules 2017, 22, 2179.

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