MTNE:A Multitext Aware Network Embedding for Predicting Drug-Drug Interaction

摘要：

　　Identifying drug-drug interactions(DDIs)is an important research topic in drug discovery.Accurate predictions of DDIs reduce the unexpected interactions during the drug development process and play a significant role in drug safety surveillance.Many existing meth-ods used drug properties to predict the unobserved interactions between drugs.However,semantic relations between drug features have seldom been considered and have resulted in low prediction accuracy.In addi-tion,incomplete annotated data and sparse drug characteristics have greatly hindered the performance of DDI predictions.In this paper,we proposed a network embedding method named MTNE(MultiText Aware Network Embedding)that considers multiple external information sources.MTNE learns the dynamic representation of the drug descrip-tion and the pharmacodynamics through a mutual attention mechanism.It effectively maps a high-dimension drug-drug interaction network to low dimension vector spaces by taking advantage of both the textual information of drugs and the topological information of the drug-drug interaction network.We conduct experiments based on the DrugBank dataset.The results show that MTNE improves the performance of DDI predictions with an AUC value of 76.1%and outperforms other state-of-the-art methods.Moreover,MTNE can also achieve high-quality predic-tion results on sparse datasets.

关键词： Drug-drug interaction Network embedding Text information Topological information Dynamic representation

作者: Fuyu Hu Chunping Ouyang Yongbin Liu Yi Bu

作者单位: School of Computer,University of South China,Hengyang 421001,Hunan,China;Hunan Medical Big Data Inte Department of Information Management,Peking University,Beijing 100871,China

会议类型: 国际会议

会议名称: 9th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC 2020)

会议地点: 郑州

会议语种:英文

页码: 306-318

在线出版日期: 2020-10-14（万方平台首次上网日期，不代表论文的发表时间）