Paper Info
Title
PC-SENE: A node embedding based method for protein complex detection
Abstract
With the accumulation of protein-protein interaction (PPI) datasets, various computational methods have been developed for identifying protein complexes from PPI networks. However, many exiting computational methods have their own limitations: supervised learning approaches need tedious effort for feature engineering and the quality measures used to guide the mining process of unsupervised methods have some drawbacks in reflecting the properties of a protein complex in PPI networks. In this work, we proposed a novel protein complex detection method, named PC-SENE. For given seeds, it uses alias sampling strategy based on protein node embedding similarities to select potential addable nodes, and makes use of a new conductance measure to decide whether to extend current candidate subgraph in order to find protein complexes. Intuitively, a well trained node embedding vector could preserve both the topological characteristics of the PPI network and the diversity of connectivity patterns of nodes in the network, and thus node embedding similarities can better reflect the relationship between nodes. The experimental results show the robustness and effectiveness of PC-SENE.
Year
DOI
Venue
2018
10.1109/BIBM.2018.8621338
2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Keywords
Field
DocType
protein complex,PPI network,seed-extension method,node embedding
Alias,Embedding,Computer science,Supervised learning,Robustness (computer science),Feature engineering,Artificial intelligence,Sampling (statistics),Machine learning
Conference
ISSN
ISBN
Citations 
2156-1125
978-1-5386-5489-7
0
PageRank 
References 
Authors
0.34
0
11
Name
Order
Citations
PageRank
Xiaoxia Liu13910.84
Zhihao Yang27315.35
Shengtian Sang3142.95
Lei Wang45613.90
Yin Zhang5368.78
Hongfei Lin6768122.52
Bo Xu744.77
Yijia Zhang811314.67
Liang Yang912042.20
Kan Xu104712.73
Jian Wang117316.74