A Semi-Supervised Network Embedding Model for Protein Complexes Detection

Wei Zhao, Jia Zhu, Min Yang, Danyang Xiao, Gabriel Pui Cheong Fung, Xiaojun Chen

AAAI 2018 pp. 8185-8186

doi:10.1609/AAAI.V32I1.12165 /aaai/2018/zhao2018aaai-semi/

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.

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Cite

Text

Zhao et al. "A Semi-Supervised Network Embedding Model for Protein Complexes Detection." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12165

Markdown

[Zhao et al. "A Semi-Supervised Network Embedding Model for Protein Complexes Detection." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/zhao2018aaai-semi/) doi:10.1609/AAAI.V32I1.12165

BibTeX

@inproceedings{zhao2018aaai-semi,
  title     = {{A Semi-Supervised Network Embedding Model for Protein Complexes Detection}},
  author    = {Zhao, Wei and Zhu, Jia and Yang, Min and Xiao, Danyang and Fung, Gabriel Pui Cheong and Chen, Xiaojun},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {8185-8186},
  doi       = {10.1609/AAAI.V32I1.12165},
  url       = {https://mlanthology.org/aaai/2018/zhao2018aaai-semi/}
}