A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks
Abstract Background The accurate identification of protein complexes is important for the understanding of cellular organization. Up to now, computational methods for protein complex detection are mostly focus on mining clusters from protein-protein interaction (PPI) networks. However, PPI data coll...
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| Format: | Article |
| Language: | English |
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BMC
2017-12-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-017-1877-4 |
| _version_ | 1818276329573842944 |
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| author | Le Ou-Yang Hong Yan Xiao-Fei Zhang |
| author_facet | Le Ou-Yang Hong Yan Xiao-Fei Zhang |
| author_sort | Le Ou-Yang |
| collection | DOAJ |
| description | Abstract Background The accurate identification of protein complexes is important for the understanding of cellular organization. Up to now, computational methods for protein complex detection are mostly focus on mining clusters from protein-protein interaction (PPI) networks. However, PPI data collected by high-throughput experimental techniques are known to be quite noisy. It is hard to achieve reliable prediction results by simply applying computational methods on PPI data. Behind protein interactions, there are protein domains that interact with each other. Therefore, based on domain-protein associations, the joint analysis of PPIs and domain-domain interactions (DDI) has the potential to obtain better performance in protein complex detection. As traditional computational methods are designed to detect protein complexes from a single PPI network, it is necessary to design a new algorithm that could effectively utilize the information inherent in multiple heterogeneous networks. Results In this paper, we introduce a novel multi-network clustering algorithm to detect protein complexes from multiple heterogeneous networks. Unlike existing protein complex identification algorithms that focus on the analysis of a single PPI network, our model can jointly exploit the information inherent in PPI and DDI data to achieve more reliable prediction results. Extensive experiment results on real-world data sets demonstrate that our method can predict protein complexes more accurately than other state-of-the-art protein complex identification algorithms. Conclusions In this work, we demonstrate that the joint analysis of PPI network and DDI network can help to improve the accuracy of protein complex detection. |
| first_indexed | 2024-12-12T22:43:55Z |
| format | Article |
| id | doaj.art-d4f70ce13d9747e6bc3e17040e60df41 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-12T22:43:55Z |
| publishDate | 2017-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-d4f70ce13d9747e6bc3e17040e60df412022-12-22T00:09:16ZengBMCBMC Bioinformatics1471-21052017-12-0118S13233410.1186/s12859-017-1877-4A multi-network clustering method for detecting protein complexes from multiple heterogeneous networksLe Ou-Yang0Hong Yan1Xiao-Fei Zhang2College of Information Engineering & Shenzhen Key Laboratory of Media Security, Shenzhen UniversityCollege of Information Engineering & Shenzhen Key Laboratory of Media Security, Shenzhen UniversitySchool of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal UniversityAbstract Background The accurate identification of protein complexes is important for the understanding of cellular organization. Up to now, computational methods for protein complex detection are mostly focus on mining clusters from protein-protein interaction (PPI) networks. However, PPI data collected by high-throughput experimental techniques are known to be quite noisy. It is hard to achieve reliable prediction results by simply applying computational methods on PPI data. Behind protein interactions, there are protein domains that interact with each other. Therefore, based on domain-protein associations, the joint analysis of PPIs and domain-domain interactions (DDI) has the potential to obtain better performance in protein complex detection. As traditional computational methods are designed to detect protein complexes from a single PPI network, it is necessary to design a new algorithm that could effectively utilize the information inherent in multiple heterogeneous networks. Results In this paper, we introduce a novel multi-network clustering algorithm to detect protein complexes from multiple heterogeneous networks. Unlike existing protein complex identification algorithms that focus on the analysis of a single PPI network, our model can jointly exploit the information inherent in PPI and DDI data to achieve more reliable prediction results. Extensive experiment results on real-world data sets demonstrate that our method can predict protein complexes more accurately than other state-of-the-art protein complex identification algorithms. Conclusions In this work, we demonstrate that the joint analysis of PPI network and DDI network can help to improve the accuracy of protein complex detection.http://link.springer.com/article/10.1186/s12859-017-1877-4Protein-protein interactionDomain-domain interactionProtein complexMulti-network clustering |
| spellingShingle | Le Ou-Yang Hong Yan Xiao-Fei Zhang A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks BMC Bioinformatics Protein-protein interaction Domain-domain interaction Protein complex Multi-network clustering |
| title | A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks |
| title_full | A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks |
| title_fullStr | A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks |
| title_full_unstemmed | A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks |
| title_short | A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks |
| title_sort | multi network clustering method for detecting protein complexes from multiple heterogeneous networks |
| topic | Protein-protein interaction Domain-domain interaction Protein complex Multi-network clustering |
| url | http://link.springer.com/article/10.1186/s12859-017-1877-4 |
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