摘要
海量增长的生物医学文献给文献挖掘技术带来巨大挑战.文中提出融合知识图谱与深度学习的药物发现方法,从已发表的文献中挖掘疾病的潜在治疗药物.首先抽取生物医学文献中实体间的关系,构造生物医学知识图谱,再通过知识图谱嵌入方法将知识图谱中的实体和关系转化为低维连续的向量,最后使用已知的药物疾病关系数据训练基于循环神经网络的药物发现模型.实验表明,文中方法不仅可以有效找到疾病的候选药物,还能提供相应的药物作用机制.
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 drugdisease related associations. The experimental results show that the proposed method can discover drugs for diseases and provide the drug mechanism of action.
作者
桑盛田
杨志豪
刘晓霞
王磊
赵迪
林鸿飞
王健
SANG Shengtian;YANG Zhihao;LIU Xiaoxia;WANG Lei;ZHAO Di;LIN Hongfei;WANG Jian(School of Computer Science and Technology, Dalian University of Technology, Dalian 116024;Institute of Health Service and Blood Research, Academy of Military Medical Sciences, Beijing 100850)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2018年第12期1103-1110,共8页
Pattern Recognition and Artificial Intelligence
基金
国家十三五重点研发计划项目(No.2016YFC0901902)
国家自然科学基金项目(No.61272373)资助~~
