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Protein complex detection via weighted ensemble clustering based on Bayesian nonnegative matrix factorization.

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 perf... Full description

Journal Title: PloS one 2013, Vol.8(5), p.e62158
Main Author: Ou-Yang, Le
Other Authors: Dai, Dao-Qing , Zhang, Xiao-Fei
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0062158
Link: http://search.proquest.com/docview/1350152085/?pq-origsite=primo
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title: Protein complex detection via weighted ensemble clustering based on Bayesian nonnegative matrix factorization.
format: Article
creator:
  • Ou-Yang, Le
  • Dai, Dao-Qing
  • Zhang, Xiao-Fei
subjects:
  • Algorithms–Methods
  • Bayes Theorem–Metabolism
  • Cluster Analysis–Metabolism
  • Protein Interaction Mapping–Metabolism
  • Reproducibility of Results–Metabolism
  • Saccharomyces Cerevisiae Proteins–Metabolism
  • Saccharomyces Cerevisiae Proteins
ispartof: PloS one, 2013, Vol.8(5), p.e62158
description: 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.
language: eng
source:
identifier: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0062158
fulltext: fulltext
issn:
  • 19326203
  • 1932-6203
url: Link


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titleProtein complex detection via weighted ensemble clustering based on Bayesian nonnegative matrix factorization.
creatorOu-Yang, Le ; Dai, Dao-Qing ; Zhang, Xiao-Fei
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identifierE-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0062158
subjectAlgorithms–Methods ; Bayes Theorem–Metabolism ; Cluster Analysis–Metabolism ; Protein Interaction Mapping–Metabolism ; Reproducibility of Results–Metabolism ; Saccharomyces Cerevisiae Proteins–Metabolism ; Saccharomyces Cerevisiae Proteins
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descriptionDetecting 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.
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