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Indexed by:期刊论文

First Author:Sang, Shengtian

Correspondence Author: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.

Co-author:Yang, Zhihao,Liu, Xiaoxia,Wang, Lei,Lin, Hongfei,Wang, Jian,Dumontier, Michel

Date of Publication:2019-01-01

Journal:IEEE ACCESS

Included Journals:SCIE、Scopus

Volume:7

Page Number:8404-8415

ISSN No.:2169-3536

Key Words:Drug discovery; biomedical knowledge graph; recurrent neural network; deep learning

Abstract: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.