Name
Abstract | ||
|---|---|---|
Detecting protein complexes from protein-protein interaction (PPI) networks allows biologists reveal the principle of cellular organization and functions. Existing computational methods try to incorporate biological evidence to enhance the quality of predicted complexes. However, it is still a challenge to integrate biological information into complexes discovery process under a unified framework. Recently, network embedding methods showed their effectiveness in graph data analysis tasks. It provides a framework for incorporating both network structure and additional node attribute information. This salient feature is particularly desirable in the context of protein complexes identification. However, none of the existing network embedding methods take node attribute proximity and high-order structure proximity into account at the same time. In this paper, we propose a novel global network embedding method, which preserves global network structure and biological information. We utilize this global representation learning method to learn vector representation for proteins. Then, we use a seed-extension clustering method to discover overlapping protein complexes with the embedding results. This novel protein complexes detection method we called GLONE. Evaluated on five real yeast PPI networks, our method outperforms the competing algorithms in terms of different evaluation metrics. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BIBM.2018.8621541 | 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
Keywords | Field | DocType |
Network embedding,ppI network,protein complexes identification | Embedding,Global network,Computer science,Artificial intelligence,Network embedding,Business process discovery,Cluster analysis,Feature learning,Machine learning,Salient,Network structure | Conference |
ISSN | ISBN | Citations |
2156-1125 | 978-1-5386-5489-7 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Xu | 1 | 1 | 2.04 |
| Kun Li | 2 | 177 | 17.51 |
| Xiaoxia Liu | 3 | 39 | 10.84 |
| Delong Liu | 4 | 0 | 0.34 |
| Yijia Zhang | 5 | 4 | 5.47 |
| Hongfei Lin | 6 | 768 | 122.52 |
| Zhihao Yang | 7 | 73 | 15.35 |
| Jian Wang | 8 | 73 | 16.74 |
| Feng Xia | 9 | 2013 | 153.69 |