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DPCMNE: detecting protein complexes from protein-protein interaction networks via multi-level network embedding
DPCMNE: detecting protein complexes from protein-protein interaction networks via multi-level network embedding
- Source :
- IEEE/ACM Transactions on Computational Biology and Bioinformatics. :1-1
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Biological functions of a cell are typically carried out through protein complexes. The detection of protein complexes is therefore of great significance for understanding the cellular organizations and protein functions. In the past decades, many computational methods have been proposed to detect protein complexes. However, most of the existing methods just search the local topological information to mine dense subgraphs as protein complexes, ignoring the global topological information. To tackle this issue, we propose the DPCMNE method to detect protein complexes via multi-level network embedding. It can preserve both the local and global topological information of biological networks. First, DPCMNE employs a hierarchical compressing strategy to recursively compress the input protein-protein interaction (PPI) network into multi-level smaller PPI networks. Then, a network embedding method is applied on these smaller PPI networks to learn protein embeddings of different levels of granularity. The embeddings learned from all the compressed PPI networks are concatenated to represent the final protein embeddings of the original input PPI network. Finally, a core-attachment based strategy is adopted to detect protein complexes in the weighted PPI network constructed by the pairwise similarity of protein embeddings. To assess the efficiency of our proposed method, DPCMNE is compared with other eight clustering algorithms on two yeast datasets. The experimental results show that the performance of DPCMNE outperforms those state-of-the-art complex detection methods in terms of F1 and F1+Acc. Furthermore, the results of functional enrichment analysis indicate that protein complexes detected by DPCMNE are more biologically significant in terms of P-score.
- Subjects :
- business.industry
Applied Mathematics
Topological information
0206 medical engineering
Network embedding
Computational Biology
Proteins
Pattern recognition
Topology (electrical circuits)
Saccharomyces cerevisiae
02 engineering and technology
Network topology
Protein protein interaction network
ComputingMethodologies_PATTERNRECOGNITION
Protein Interaction Mapping
Genetics
Protein Interaction Maps
Granularity
Artificial intelligence
Cluster analysis
business
Algorithms
020602 bioinformatics
Biological network
Biotechnology
Subjects
Details
- ISSN :
- 23740043 and 15455963
- Database :
- OpenAIRE
- Journal :
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Accession number :
- edsair.doi.dedup.....d187cff9434449a1cb94217f97878ac5
- Full Text :
- https://doi.org/10.1109/tcbb.2021.3050102