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Protein Complex Detection via Weighted Ensemble Clustering Based on Bayesian Nonnegative Matrix Factorization

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  • Le Ou-Yang
  • Dao-Qing Dai
  • Xiao-Fei Zhang

Abstract

: Detecting protein complexes from protein-protein interaction (PPI) networks is a challenging task in computational biology. A vast number of computational methods have been proposed to undertake this task. However, each computational method is developed to capture one aspect of the network. The performance of different methods on the same network can differ substantially, even the same method may have different performance on networks with different topological characteristic. The clustering result of each computational method can be regarded as a feature that describes the PPI network from one aspect. It is therefore desirable to utilize these features to produce a more accurate and reliable clustering. In this paper, a novel Bayesian Nonnegative Matrix Factorization(NMF)-based weighted Ensemble Clustering algorithm (EC-BNMF) is proposed to detect protein complexes from PPI networks. We first apply different computational algorithms on a PPI network to generate some base clustering results. Then we integrate these base clustering results into an ensemble PPI network, in the form of weighted combination. Finally, we identify overlapping protein complexes from this network by employing Bayesian NMF model. When generating an ensemble PPI network, EC-BNMF can automatically optimize the values of weights such that the ensemble algorithm can deliver better results. Experimental results on four PPI networks of Saccharomyces cerevisiae well verify the effectiveness of EC-BNMF in detecting protein complexes. EC-BNMF provides an effective way to integrate different clustering results for more accurate and reliable complex detection. Furthermore, EC-BNMF has a high degree of flexibility in the choice of base clustering results. It can be coupled with existing clustering methods to identify protein complexes.

Suggested Citation

  • Le Ou-Yang & Dao-Qing Dai & Xiao-Fei Zhang, 2013. "Protein Complex Detection via Weighted Ensemble Clustering Based on Bayesian Nonnegative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-18, May.
  • Handle: RePEc:plo:pone00:0062158
    DOI: 10.1371/journal.pone.0062158
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. Yong-Yeol Ahn & James P. Bagrow & Sune Lehmann, 2010. "Link communities reveal multiscale complexity in networks," Nature, Nature, vol. 466(7307), pages 761-764, August.
    3. Xiao-Fei Zhang & Dao-Qing Dai & Le Ou-Yang & Meng-Yun Wu, 2012. "Exploring Overlapping Functional Units with Various Structure in Protein Interaction Networks," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-11, August.
    4. Oron Vanunu & Oded Magger & Eytan Ruppin & Tomer Shlomi & Roded Sharan, 2010. "Associating Genes and Protein Complexes with Disease via Network Propagation," PLOS Computational Biology, Public Library of Science, vol. 6(1), pages 1-9, January.
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    Cited by:

    1. Amir Lakizadeh & Saeed Jalili, 2016. "BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-16, July.

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