Towards Gene Function Prediction via Multi-Networks Representation Learning

AAAI 2019 pp. 10069-10070

doi:10.1609/AAAI.V33I01.330110069 /aaai/2019/xue2019aaai-gene/

Abstract

Multi-networks integration methods have achieved prominent performance on many network-based tasks, but these approaches often incur information loss problem. In this paper, we propose a novel multi-networks representation learning method based on semi-supervised autoencoder, termed as DeepMNE, which captures complex topological structures of each network and takes the correlation among multinetworks into account. The experimental results on two realworld datasets indicate that DeepMNE outperforms the existing state-of-the-art algorithms.

PDF AAAI Semantic Scholar

Cite

Text

Xue et al. "Towards Gene Function Prediction via Multi-Networks Representation Learning." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.330110069

Markdown

[Xue et al. "Towards Gene Function Prediction via Multi-Networks Representation Learning." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/xue2019aaai-gene/) doi:10.1609/AAAI.V33I01.330110069

BibTeX

@inproceedings{xue2019aaai-gene,
  title     = {{Towards Gene Function Prediction via Multi-Networks Representation Learning}},
  author    = {Xue, Hansheng and Peng, Jiajie and Shang, Xuequn},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {10069-10070},
  doi       = {10.1609/AAAI.V33I01.330110069},
  url       = {https://mlanthology.org/aaai/2019/xue2019aaai-gene/}
}