Towards gene function prediction via multi-networks representation learning

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 multi-networks into account. The experimental results on two real-world datasets indicate that DeepMNE outperforms the existing state-of-the-art algorithms.