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Indexed by:会议论文

First Author:Zhang, Xianchao

Co-author:Gao, Peixu,Sun, Maohua,Zong, Linlin,Xu, Bo

Date of Publication:2019-04-14

Included Journals:EI

Volume:11607 LNAI

Page Number:164-178

Abstract:Protein complexes play an important role for scientists to explore the secrets of cell and life. Most of the existing protein complexes detection methods utilize traditional clustering algorithms on protein-protein interaction (PPI) networks. However, due to the complexity of the network structure, traditional clustering methods cannot capture the network information effectively. Therefore, how to extract information from high-dimensional networks has become a challenge. In this paper, we propose a novel protein complexes detection method called DANE, which uses a deep neural network to maintain the primary information. Furthermore, we use a deep autoencoder framework to implement the embedding process, which preserves the network structure and the additional biological information. Then, we use the clustering method based on the core-attachment principle to get the prediction result. The experiments on six yeast datasets with five other detection methods show that our method gets better performance. ? 2019, Springer Nature Switzerland AG.