Towards Gene Function Prediction via Multi-Networks Representation Learning

Authors

  • Hansheng Xue Northwestern Polytechnical University
  • Jiajie Peng Northwestern Polytechnical University
  • Xuequn Shang Northwestern Polytechnical University

DOI:

https://doi.org/10.1609/aaai.v33i01.330110069

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.

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Published

2019-07-17

How to Cite

Xue, H., Peng, J., & Shang, X. (2019). Towards Gene Function Prediction via Multi-Networks Representation Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 10069-10070. https://doi.org/10.1609/aaai.v33i01.330110069

Issue

Section

Student Abstract Track