Name
Abstract | ||
|---|---|---|
Advanced biological technologies are producing large-scale protein-protein interaction (PPI) data at an ever increasing pace, which enable us to identify protein complexes from PPI networks. Pair-wise protein interactions can be modeled as a graph, where vertices represent proteins and edges represent PPIs. However most of current algorithms detect protein complexes based on deterministic graphs, whose edges are either present or absent. Neighboring information is neglected in these methods. Based on the uncertain graph model, we propose the concept of expected density to assess the density degree of a subgraph, the concept of relative degree to describe the relationship between a protein and a subgraph in a PPI network. We develop an algorithm called DCU (detecting complex based on uncertain graph model) to detect complexes from PPI networks. In our method, the expected density combined with the relative degree is used to determine whether a subgraph represents a complex with high cohesion and low coupling. We apply our method and the existing competing algorithms to two yeast PPI networks. Experimental results indicate that our method performs significantly better than the state-of-the-art methods and the proposed model can provide more insights for future study in PPI networks. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TCBB.2013.2297915 | Computational Biology and Bioinformatics, IEEE/ACM Transactions |
Keywords | Field | DocType |
bioinformatics,data mining,graph theory,microorganisms,molecular biophysics,proteins,DCU algorithm,PPI data,advanced biological technologies,competing algorithms,deterministic graphs,expected density,graph edges,graph vertices,pair-wise protein interaction model,protein complex cohesion,protein complex coupling,protein complex detection algorithms,protein complex identification,protein-protein interaction,protein-subgraph relationship,relative degree,subgraph density degree assessment,uncertain graph model,yeast PPI networks,Uncertain graph model,expected density,protein complex,relative degree | Graph,Protein–protein interaction,Vertex (geometry),Cohesion (computer science),Protein engineering,Computer science,Artificial intelligence,Bioinformatics,Cluster analysis,Coupling (computer programming),Machine learning,Graph model | Journal |
Volume | Issue | ISSN |
11 | 3 | 1545-5963 |
Citations | PageRank | References |
5 | 0.40 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bihai Zhao | 1 | 24 | 3.74 |
| Jianxin Wang | 2 | 2163 | 283.94 |
| Min Li | 3 | 752 | 66.03 |
| FangXiang Wu | 4 | 760 | 76.89 |
| Yi Pan | 5 | 2507 | 203.23 |