期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 19, 期 3, 页码 1592-1602出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2021.3050102
关键词
Proteins; Biology; Benchmark testing; Network topology; Topology; Organizations; Nuclear magnetic resonance; Protein complex; protein-protein interaction network; clustering; network embedding
类别
资金
- National Natural Science Foundation of China [61832019]
- Hunan Provincial Science and Technology Program [2019CB1007]
- Fundamental Research Funds for the Central Universities, CSU [2282019SYLB004]
- 2019 IEEE International Conference on Bioinformatics and Biomedicine [BIBM2019) [57]]
Protein complexes play a crucial role in the biological functions of cells. This study proposes a method called DPCMNE to detect protein complexes using multi-level network embedding, which preserves both the local and global topological information of biological networks. Experimental results show that DPCMNE outperforms other existing methods in terms of F1 and F1+Acc, and the protein complexes detected by DPCMNE are biologically more significant.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据