A Method Combining Knowledge Graph and Deep Learning for Drug Discovery
SANG Shengtian1, YANG Zhihao1, LIU Xiaoxia1, WANG Lei2, ZHAO Di1, LIN Hongfei1, WANG Jian1
1.School of Computer Science and Technology, Dalian University of Technology, Dalian 116024 2.Institute of Health Service and Blood Research, Academy of Military Medical Sciences, Beijing 100850
Abstract The massive growing amount of biomedical literature brings huge challenges for data mining. In this paper, a method combining knowledge graph and deep learning is proposed to discover potential therapeutic drugs for disease of interest. Firstly, a biomedical knowledge graph is constructed with the relations extracted from biomedical literature. Then, the entities and relations of the knowledge graph are converted into low dimension continuous embeddings by knowledge graph embedding method. Finally, a recurrent neural network based drug discovery model is trained by using the known drug-disease related associations. The experimental results show that the proposed method can discover drugs for diseases and provide the drug mechanism of action.
Fund:Supported by National Key Research and Development Project of China(No.2016YFC0901902), National Natural Science Foundation of China(No.61272373)
About author:: (SANG Shengtian, Ph.D. candidate. His re-search interests include data mining and lite-rature-based discovery.) (YANG Zhihao(Corresponding author), Ph.D., professor. His research interests include natural language processing and data mining.) (LIU Xiaoxia, Ph.D. candidate. Her research interests include data mining and natural language processing.) (WANG Lei, Ph.D., professor. Her research interests include biomedical informa-tics.) (ZHAO Di, Ph.D. candidate. His research interests include data mining and natural language processing.) (LIN Hongfei, Ph.D., professor. His research interests include social media data mining and artificial intelligence.) (WANG Jian, Ph.D., professor. Her research interests include natural language processing.)
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2018, Vol. 31 
