Towards Gene Function Prediction via Multi-Networks Representation Learning
DOI:
https://doi.org/10.1609/aaai.v33i01.330110069Abstract
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
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Student Abstract Track