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论文类型：期刊论文

第一作者：Sang, Shengtian

通讯作者：Yang, ZH (reprint author), Dalian Univ Technol, Coll Comp Sci & Technol, Dalian 116023, Peoples R China.; Wang, L (reprint author), Beijing Inst Hlth Adm & Med Informat, Beijing 100191, Peoples R China.

合写作者：Yang, Zhihao,Liu, Xiaoxia,Wang, Lei,Lin, Hongfei,Wang, Jian,Dumontier, Michel

发表时间：2019-01-01

发表刊物：IEEE ACCESS

收录刊物：SCIE、Scopus

卷号：7

页面范围：8404-8415

ISSN号：2169-3536

关键字：Drug discovery; biomedical knowledge graph; recurrent neural network; deep learning

摘要：Drug discovery is the process by which new candidate medications are discovered. Developing a new drug is a lengthy, complex, and expensive process. Here, in this paper, we propose a biomedical knowledge graph embedding-based recurrent neural network method called GrEDeL, which discovers potential drugs for diseases by mining published biomedical literature. GrEDeL first builds a biomedical knowledge graph by exploiting the relations extracted from biomedical abstracts. Then, the graph data are converted into a low dimensional space by leveraging the knowledge graph embedding methods. After that, a recurrent neural network model is trained by the known drug therapies which are represented by graph embeddings. Finally, it uses the learned model to discover candidate drugs for diseases of interest from biomedical literature. The experimental results show that our method could not only effectively discover new drugs by mining literature, but also could provide the corresponding mechanism of actions for the candidate drugs. It could be a supplementary method for the current traditional drug discovery methods.