教授 博士生导师 硕士生导师
主要任职: 科学技术研究院国防重大项目办公室主任
性别: 男
毕业院校: 中国科技大学
学位: 博士
所在单位: 软件学院、国际信息与软件学院
学科: 计算机应用技术. 软件工程
电子邮箱: xczhang@dlut.edu.cn
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论文类型: 会议论文
第一作者: Zhang, Xianchao
合写作者: Gao, Peixu,Sun, Maohua,Zong, Linlin,Xu, Bo
发表时间: 2019-04-14
收录刊物: EI
卷号: 11607 LNAI
页面范围: 164-178
摘要: 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.