融合知识图谱与深度学习的药物发现方法

融合知识图谱与深度学习的药物发现方法桑盛田1杨志豪1刘晓霞1王磊2赵迪1林鸿飞1王健1

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.

关键词数据挖掘,生物医学知识图谱,深度学习,循环神经网络

引用格式桑盛田,杨志豪,刘晓霞,王磊,赵迪,林鸿飞,王健.融合知识图谱与深度学习的药物发现方法.模式识别与人工智能,2018,31(12):1103-1110.

DOI10.16451/https://www.360docs.net/doc/b35736807.html,ki.issn1003-6059.201812005中图法分类号TP391

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 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.

Key Words Data Mining,Biomedical Knowledge Graph,Deep Learning,Recurrent Neural Network Citation SANG S T,YANG Z H,LIU X X,WANG L,ZHAO D,LIN H F,WANG J.A Method Combining Knowledge Graph and Deep Learning for Drug Discovery.Pattern Recognition and

Artificial Intelligence,2018,31(12):1103-1110.

收稿日期:2018-10-25;录用日期:2018-12-12 Manuscript received October25,2018; accepted December12,2018

国家十三五重点研发计划项目(No.2016YFC0901902)二国家自然科学基金项目(No.61272373)资助Supported by National Key Research and Development Project of China(No.2016YFC0901902),National Natural Science Foun-dation of China(No.61272373)

本文责任编委马少平Recommended by Associate Editor MA Shaoping

1.大连理工大学计算机科学与技术学院大连116024

2.中国人民解放军军事医学科学院卫生勤务与血液研究所北京100850

药物发现(Drug Discovery)是一个周期漫长且代价昂贵的过程.开发一款新药平均需要14年和

18亿美金[1].相反地,从文献中挖掘新的药物是一个周期相对较短且经济的方法.目前,PubMed数据库收录超过2400万篇生物医学摘要,通过挖掘这些海量的医学文献可以找到某些疾病潜在的治疗方法[2].例如在1986年以前雷诺士病(Raynaud Disea-1.School of Computer Science and Technology,Dalian Universi-ty of Technology,Dalian116024

2.Institute of Health Service and Blood Research,Academy of Military Medical Sciences,Beijing100850

第31卷第12期模式识别与人工智能Vol.31 No.12 2018年12月Pattern Recognition and Artificial Intelligence Dec.2018

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