A Semi-Supervised Network Embedding Model for Protein Complexes Detection

Authors

  • Wei Zhao SIAT, Chinese Academy of Sciences
  • Jia Zhu South China Normal University
  • Min Yang SIAT, Chinese Academy of Sciences
  • Danyang Xiao South China Normal University
  • Gabriel Pui Cheong Fung The Chinese University of Hong Kong
  • Xiaojun Chen Shenzhen University

DOI:

https://doi.org/10.1609/aaai.v32i1.12165

Keywords:

Network Embedding, Protein Complexes, Semi-Supervised

Abstract

Protein complex is a group of associated polypeptide chains which plays essential roles in biological process. Given a graph representing protein-protein interactions (PPI) network, it is critical but non-trivial to detect protein complexes.In this paper, we propose a semi-supervised network embedding model by adopting graph convolutional networks to effectively detect densely connected subgraphs. We conduct extensive experiment on two popular PPI networks with various data sizes and densities. The experimental results show our approach achieves state-of-the-art performance.

AAAI 2018 Proceedings Cover

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Published

2018-04-29

How to Cite

Zhao, W., Zhu, J., Yang, M., Xiao, D., Fung, G. P. C., & Chen, X. (2018). A Semi-Supervised Network Embedding Model for Protein Complexes Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.12165

Issue

Vol. 32 No. 1 (2018): Thirty-Second AAAI Conference on Artificial Intelligence

Section

Student Abstract Track