杨志豪

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:计算机科学与技术学院

电子邮箱:yangzh@dlut.edu.cn

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Identifying protein complexes based on node embeddings obtained from protein-protein interaction networks

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论文类型:期刊论文

第一作者:Liu, Xiaoxia

通讯作者:Yang, ZH (reprint author), Dalian Univ Technol, Coll Comp Sci & Technol, Dalian 116024, Liaoning, Peoples R China.; Wang, L (reprint author), Beijing Inst Hlth Adm & Med Informat, Beijing 100850, Peoples R China.

合写作者:Yang, Zhihao,Sang, Shengtian,Zhou, Ziwei,Wang, Lei,Zhang, Yin,Lin, Hongfei,Wang, Jian,Xu, Bo

发表时间:2018-09-21

发表刊物:BMC BIOINFORMATICS

收录刊物:PubMed、SCIE

卷号:19

期号:1

页面范围:332

ISSN号:1471-2105

关键字:Node embeddings; Random forest; Supervised learning method; Protein complex detection

摘要:Background: Protein complexes are one of the keys to deciphering the behavior of a cell system. During the past decade, most computational approaches used to identify protein complexes have been based on discovering densely connected subgraphs in protein-protein interaction (PPI) networks. However, many true complexes are not dense subgraphs and these approaches show limited performances for detecting protein complexes from PPI networks.
   Results: To solve these problems, in this paper we propose a supervised learning method based on network node embeddings which utilizes the informative properties of known complexes to guide the search process for new protein complexes. First, node embeddings are obtained from human protein interaction network. Then the protein interactions are weighted through the similarities between node embeddings. After that, the supervised learning method is used to detect protein complexes. Then the random forest model is used to filter the candidate complexes in order to obtain the final predicted complexes. Experimental results on real human and yeast protein interaction networks show that our method effectively improves the performance for protein complex detection.
   Conclusions: We provided a new method for identifying protein complexes from human and yeast protein interaction networks, which has great potential to benefit the field of protein complex detection.