基于多源语义知识图谱的药物知识发现:以药物重定位为实证<sup>*</sup>

doi:10.11925/infotech.2096-3467.2021.1364

数据分析与知识发现

2022, Vol. 6

Issue (7): 87-98 https://doi.org/10.11925/infotech.2096-3467.2021.1364

研究论文

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基于多源语义知识图谱的药物知识发现:以药物重定位为实证^*

张晗(

),安欣宇,刘春鹤

中国医科大学健康管理学院沈阳 110122

Building Multi-Source Semantic Knowledge Graph for Drug Repositioning

Zhang Han(

),An Xinyu,Liu Chunhe

School of Health Management, China Medical University, Shenyang 110122, China

摘要
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摘要

【目的】探讨全数据集、跨数据平台的语义知识图谱构建方法,开展基于知识图谱的药物知识发现研究。【方法】获取PubMed、DrugBank、CTD等数据库的知识关联,通过知识融合、属性定义构建语义知识图谱,以药物重定位为实证,采用路径搜索和链路预测两种方法推理药物在肿瘤治疗中的新用途。【结果】利用知识图谱能有效地进行药物发现,两种方法的总体预测效果相近,路径发现预测F值为0.57, 略高于链路预测（0.56）。此外,药物与适应症之间存在的路径数越多,预测阳性的可能性越大。【局限】实证研究知识推理机制建立在已有知识关联基础上,难以对无靶点信息的新药进行挖掘;同时庞大的数据体量难以实现知识图谱的动态更新。【结论】融合多源数据集构建的知识图谱能有效地发现药物新适应症,提升药物研发效率,为药物知识发现提供新思路。

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	张晗
	安欣宇
	刘春鹤

关键词 ：知识发现, 药物重定位, 语义模型, 知识图谱

Abstract：

[Objective] This paper constructs a cross-platform semantic knowledge graph with whole datasets, which helps us find novel drug knowledge. [Methods] First, we developed a new model for the proposed knowledge graph, which integrated semantic relations from PubMed, DrugBank and CTD, as well as knowledge fusion and attribute definition. Then, we conducted drug repositioning with pathway identification and link predication to discover new treatments for cancers. [Results] The F-score of pathway identification (0.57) was better than that of the linkage predication (0.56). The more pathways existing between drugs and indications, the greater possibility of predicting positively. [Limitations] Since the reasoning mechanism was based on the existing associations among knowledge units, it is hard to discover the novel indications for drugs without the known targets. It is difficult to update knowledge graph dynamically due to the huge data volume. [Conclusions] The proposed knowledge graph could effectively find new drug indications as well as improve the efficiency for drug research and development.

Key words： Knowledge Discovery Drug Repositioning Semantic Model Knowledge Graph

收稿日期: 2021-12-01 出版日期: 2022-08-24

ZTFLH:

R9 G353

基金资助:*辽宁省社会科学规划基金项目的研究成果之一(L19BTQ006)

通讯作者: 张晗,ORCID:0000-0002-9085-5875 E-mail: zhanghan@cmu.edu.cn

引用本文:

张晗, 安欣宇, 刘春鹤. 基于多源语义知识图谱的药物知识发现:以药物重定位为实证^*[J]. 数据分析与知识发现, 2022, 6(7): 87-98.
Zhang Han, An Xinyu, Liu Chunhe. Building Multi-Source Semantic Knowledge Graph for Drug Repositioning. Data Analysis and Knowledge Discovery, 2022, 6(7): 87-98.

链接本文:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2021.1364 或 https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2022/V6/I7/87

Fig.1 药物知识发现图谱构建框架

Table 1 知识图谱实体类别-语义类型对照

Fig.2 知识图谱示意

Table 2 药物数据概况

Fig.3 预测图谱药物发现搜索路径

Table 3 药物预测评价结果

Table 4 4种排序方法的MAP

Fig.4 两组疾病与关联靶点分布

Fig.5 伊曲康唑发现结果

[1]	胡正银, 刘蕾蕾, 代冰, 等. 基于领域知识图谱的生命医学学科知识发现探析[J]. 数据分析与知识发现, 2020, 4(11): 1-14.
[1]	( Hu Zhengyin, Liu Leilei, Dai Bing, et al. Discovering Subject Knowledge in Life and Medical Sciences with Knowledge Graph[J]. Data Analysis and Knowledge Discovery, 2020, 4(11): 1-14.)
[2]	Swanson D R. Fish Oil, Raynaud’s Syndrome, and Undiscovered Public Knowledge[J]. Perspectives in Biology and Medicine, 1986, 30(1): 7-18. pmid: 3797213
[3]	李东巧, 陈芳, 韩涛, 等. 基于二模复杂网络的隐性知识发现方法研究——以潜在药物靶点挖掘为例[J]. 图书情报工作, 2020, 64(21): 120-129. doi: 10.13266/j.issn.0252-3116.2020.21.015
[3]	( Li Dongqiao, Chen Fang, Han Tao, et al. Research on the Tacit Knowledge Discovery Based on Two-Mode Complex Network—Take Mining Potential Drug Targets as an Example[J]. Library and Information Service, 2020, 64(21): 120-129.) doi: 10.13266/j.issn.0252-3116.2020.21.015
[4]	Ma J, Wang J, Ghoraie L S, et al. A Comparative Study of Cluster Detection Algorithms in Protein-Protein Interaction for Drug Target Discovery and Drug Repurposing[J]. Frontiers in Pharmacology, 2019, 10: 109. doi: 10.3389/fphar.2019.00109
[5]	Hristovski D, Kastrin A, Dinevski D, et al. Using Literature-Based Discovery to Explain Adverse Drug Effects[J]. Journal of Medical Systems, 2016, 40(8): 185. doi: 10.1007/s10916-016-0544-z pmid: 27318993
[6]	Shang N, Xu H, Rindflesch T C, et al. Identifying Plausible Adverse Drug Reactions Using Knowledge Extracted from the Literature[J]. Journal of Biomedical Informatics, 2014, 52: 293-310. doi: 10.1016/j.jbi.2014.07.011 pmid: 25046831
[7]	Xu R, Wang Q Q. Toward Creation of a Cancer Drug Toxicity Knowledge Base: Automatically Extracting Cancer Drug—Side Effect Relationships from the Literature[J]. Journal of the American Medical Informatics Association, 2013, 21(1): 90-96. doi: 10.1136/amiajnl-2012-001584
[8]	范馨月, 崔雷. 基于文本挖掘的药物副作用知识发现研究[J]. 数据分析与知识发现, 2018, 2(3): 79-86.
[8]	( Fan Xinyue, Cui Lei. Using Text Mining to Discover Drug Side Effects: Case Study of PubMed[J]. Data Analysis and Knowledge Discovery, 2018, 2(3): 79-86.)
[9]	Zhang R, Cairelli M J, Fiszman M, et al. Using Semantic Predications to Uncover Drug-Drug Interactions in Clinical Data[J]. Journal of Biomedical Informatics, 2014, 49: 134-147. doi: 10.1016/j.jbi.2014.01.004 pmid: 24448204
[10]	Du J, Li X Y. A Knowledge Graph of Combined Drug Therapies Using Semantic Predications from Biomedical Literature: Algorithm Development[J]. JMIR Medical Informatics, 2020, 8(4): e18323. doi: 10.2196/18323
[11]	Zhang R, Hristovski D, Schutte D, et al. Drug Repurposing for COVID-19 via Knowledge Graph Completion[J]. Journal of Biomedical Informatics, 2021, 115: 103696. doi: 10.1016/j.jbi.2021.103696
[12]	魏星, 胡德华, 易敏寒, 等. 基于数据立方体挖掘疾病-基因-药物新关联[J]. 数据分析与知识发现, 2017, 1(10): 94-104.
[12]	( Wei Xing, Hu Dehua, Yi Minhan, et al. Extracting Disease-Gene-Drug Correlations Based on Data Cube[J]. Data Analysis and Knowledge Discovery, 2017, 1(10): 94-104.)
[13]	Gachloo M, Wang Y X, Xia J B. A Review of Drug Knowledge Discovery Using BioNLP and Tensor or Matrix Decomposition[J]. Genomics & Informatics, 2019, 17(2): e18.
[14]	Hahn U, Oleynik M. Medical Information Extraction in the Age of Deep Learning[J]. Yearbook of Medical Informatics, 2020, 29(1): 208-220. doi: 10.1055/s-0040-1702001 pmid: 32823318
[15]	Aronson A R, Lang F M. An Overview of MetaMap: Historical Perspective and Recent Advances[J]. Journal of the American Medical Informatics Association, 2010, 17(3): 229-236. doi: 10.1136/jamia.2009.002733 pmid: 20442139
[16]	Wei C H, Allot A, Leaman R, et al. PubTator Central: Automated Concept Annotation for Biomedical Full Text Articles[J]. Nucleic Acids Research, 2019, 47(W1): W587-W593. doi: 10.1093/nar/gkz389
[17]	Wang Y S, Wang L W, Rastegar-Mojarad M, et al. Clinical Information Extraction Applications: A Literature Review[J]. Journal of Biomedical Informatics, 2018, 77: 34-49. doi: 10.1016/j.jbi.2017.11.011
[18]	Bejan C A, Wei W Q, Denny J C. Assessing the Role of a Medication-Indication Resource in the Treatment Relation Extraction from Clinical Text[J]. Journal of the American Medical Informatics Association, 2015, 22(e1): e162-e176. doi: 10.1136/amiajnl-2014-002954
[19]	Kilicoglu H, Rosemblat G, Fiszman M, et al. Broad-Coverage Biomedical Relation Extraction with SemRep[J]. BMC Bioinformatics, 2020, 21(1): 188. doi: 10.1186/s12859-020-3517-7 pmid: 32410573
[20]	曹明宇, 李青青, 杨志豪, 等. 基于知识图谱的原发性肝癌知识问答系统[J]. 中文信息学报, 2019, 33(6): 88-93.
[20]	( Cao Mingyu, Li Qingqing, Yang Zhihao, et al. A Question Answering System for Primary Liver Cancer Based on Knowledge Graph[J]. Journal of Chinese Information Processing, 2019, 33(6): 88-93.)
[21]	Zhang R, Cairelli M J, Fiszman M, et al. Exploiting Literature-Derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs[J]. Cancer Informatics, 2014, 13(S1): 103-111.
[22]	王雪, 杨雪梅, 李沛鑫, 等. 基于语义模型的药物矛盾知识发现[J]. 情报杂志, 2020, 39(7): 159-165.
[22]	( Wang Xue, Yang Xuemei, Li Peixin, et al. Contradiction Knowledge Discovery of Drugs Based on Semantic Model[J]. Journal of Intelligence, 2020, 39(7): 159-165.)
[23]	蔡妙芝, 李晓瑛, 赵嘉玮, 等. 基于SPO语义三元组的疾病知识发现[J]. 数据分析与知识发现, 2022, 6(1): 134-144.
[23]	( Cai Miaozhi, Li Xiaoying, Zhao Jiawei, et al. Disease Knowledge Discovery Based on SPO Predications[J]. Data Analysis and Knowledge Discovery, 2022, 6(1): 134-144.)
[24]	刘晓娟, 李广建, 化柏林. 知识融合: 概念辨析与界说[J]. 图书情报工作, 2016, 60(13): 13-19. doi: 10.13266/j.issn.0252-3116.2016.13.002
[24]	( Liu Xiaojuan, Li Guangjian, Hua Bolin. Knowledge Fusion: From the Conceptual Understanding to the System Construction[J]. Library and Information Service, 2016, 60(13): 13-19.) doi: 10.13266/j.issn.0252-3116.2016.13.002
[25]	Zhu Q, Nguyễn Ð T, Sheils T, et al. Scientific Evidence Based Rare Disease Research Discovery with Research Funding Data in Knowledge Graph[J]. Orphanet Journal of Rare Diseases, 2021, 16(1): 483. doi: 10.1186/s13023-021-02120-9
[26]	Yan V K C, Li X D, Ye X X, et al. Drug Repurposing for the Treatment of COVID-19: A Knowledge Graph Approach[J]. Advanced Therapeutics, 2021, 4(7): 2100055.
[27]	王丽伟, 王伟, 高玉堂, 等. 领域本体映射的语义互联方法研究——以药物本体为例[J]. 图书情报工作, 2013, 57(17): 21-25. doi: 10.7536/j.issn.0252-3116.2013.17.004
[27]	( Wang Liwei, Wang Wei, Gao Yutang, et al. Semantic Interconnection Method for Mapping Multiple Domain Ontologies: Exemplified by Drug Ontology[J]. Library and Information Service, 2013, 57(17): 21-25.) doi: 10.7536/j.issn.0252-3116.2013.17.004
[28]	钱庆, 洪娜, 李姣. 面向药物研发的大规模数据语义整合与挖掘模式探索[J]. 数字图书馆论坛, 2014(3): 19-25.
[28]	( Qian Qing, Hong Na, Li Jiao. Research on Big Data Semantic Integration and Mining Pattern for Drug Discovery[J]. Digital Library Forum, 2014(3): 19-25.)
[29]	代冰, 胡正银. 基于文献的知识发现新近研究综述[J]. 数据分析与知识发现, 2021, 5(4): 1-12.
[29]	( Dai Bing, Hu Zhengyin. Review of Studies on Literature-Based Discovery[J]. Data Analysis and Knowledge Discovery, 2021, 5(4): 1-12.)
[30]	桑盛田, 杨志豪, 刘晓霞, 等. 融合知识图谱与深度学习的药物发现方法[J]. 模式识别与人工智能, 2018, 31(12): 1103-1110. doi: 10.16451/j.cnki.issn1003-6059.201812005
[30]	( Sang Shengtian, Yang Zhihao, Liu Xiaoxia, et al. A Method Combining Knowledge Graph and Deep Learning for Drug Discovery[J]. Pattern Recognition and Artificial Intelligence, 2018, 31(12): 1103-1110.) doi: 10.16451/j.cnki.issn1003-6059.201812005
[31]	张晗, 赵玉虹. 医学文献语义共词知识网的构建: 方法与实证[J]. 图书情报工作, 2016, 60(11): 135-142. doi: 10.13266/j.issn.0252-3116.2016.11.019
[31]	( Zhang Han, Zhao Yuhong. Construction of Semantic Co-Word Knowledge Network for Medical Literature: Method and Empirical Study[J]. Library and Information Service, 2016, 60(11): 135-142.) doi: 10.13266/j.issn.0252-3116.2016.11.019
[32]	张晗, 赵玉虹. 基于语义图的医学多文档摘要提取模型构建[J]. 图书情报工作, 2017, 61(8): 112-119. doi: 10.13266/j.issn.0252-3116.2017.08.014
[32]	( Zhang Han, Zhao Yuhong. Constructing Semantic Graph Based on Summary Extracting Model for Multiple Medical Documents[J]. Library and Information Service, 2017, 61(8): 112-119.) doi: 10.13266/j.issn.0252-3116.2017.08.014
[33]	隗玲, 胡正银, 庞弘燊, 等. 基于“主语-谓语-宾语”三元组的知识发现研究——以诱导多能干细胞领域为例[J]. 数字图书馆论坛, 2017(9): 28-34.
[33]	( Wei Ling, Hu Zhengyin, Pang Hongshen, et al. Study on Knowledge Discovery Based on “Subject-Predication-Object” Predications: A Case Study of Induced Pluripotent Stem Cells[J]. Digital Library Forum, 2017(9): 28-34.)
[34]	马代川, 罗代兵. 基于网络链路预测的药物分子重定位研究[J]. 化学研究与应用, 2020, 32(5): 752-757.
[34]	( Ma Daichuan, Luo Daibing. Link Prediction Algorithm for Drug Repositioning Based on Structural Feature[J]. Chemical Research and Application, 2020, 32(5): 752-757.)
[35]	Weeber M, Klein H, den Berg L T W D J V, et al. Using Concepts in Literature-Based Discovery: Simulating Swanson’s Raynaud-Fish Oil and Migraine-Magnesium Discoveries[J]. Journal of the American Society for Information Science and Technology, 2001, 52(7): 548-557. doi: 10.1002/asi.1104
[36]	Yetisgen-Yildiz M, Pratt W. A New Evaluation Methodology for Literature-Based Discovery Systems[J]. Journal of Biomedical Informatics, 2009, 42(4): 633-643. doi: 10.1016/j.jbi.2008.12.001 pmid: 19124086
[37]	Yang H T, Ju J H, Wong Y T, et al. Literature-Based Discovery of New Candidates for Drug Repurposing[J]. Briefings in Bioinformatics, 2016, 18(3): 488-497.
[38]	Wishart D S, Knox C, Guo A C, et al. DrugBank: A Comprehensive Resource for in Silico Drug Discovery and Exploration[J]. Nucleic Acids Research, 2006, 34(Database issue): D668-D672.
[39]	Davis A P, Grondin C J, Johnson R J, et al. Comparative Toxicogenomics Database(CTD): Update 2021[J]. Nucleic Acids Research, 2020, 49(D1): D1138-D1143. doi: 10.1093/nar/gkaa891
[40]	Zhang H, Fiszman M, Shin D, et al. Clustering Cliques for Graph-Based Summarization of the Biomedical Research Literature[J]. BMC Bioinformatics, 2013, 14: 182. doi: 10.1186/1471-2105-14-182 pmid: 23742159
[41]	Cilibrasi R L, Vitanyi P M B. The Google Similarity Distance[J]. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(3): 370-383. doi: 10.1109/TKDE.2007.48
[42]	Gong F M, Ma Y H, Gong W J, et al. Neo4j Graph Database Realizes Efficient Storage Performance of Oilfield Ontology[J]. PLoS One, 2018, 13(11): e0207595.
[43]	宫雪, 崔雷. 基于医学主题词共现网络的链接预测研究[J]. 情报杂志, 2018, 37(1): 66-71.
[43]	( Gong Xue, Cui Lei. Link Prediction in MeSH Terms Co-Occurring Networks[J]. Journal of Intelligence, 2018, 37(1): 66-71.)
[44]	张若凡, 赵子琪, 安明扬. 粤港澳大湾区专利合作申请网络结构及链路预测[J]. 中国发明与专利, 2021, 18(3): 26-33.
[44]	( Zhang Ruofan, Zhao Ziqi, An Mingyang. Structure Analysis and Link Prediction of Patent Technology Cooperation Network in Guangdong-Hong Kong-Macao Greater Bay Area[J]. China Invention & Patent, 2021, 18(3): 26-33.)
[45]	Adamic L A, Adar E. Friends and Neighbors on the Web[J]. Social Networks, 2003, 25(3): 211-230. doi: 10.1016/S0378-8733(03)00009-1
[46]	Shim J S, Liu J O. Recent Advances in Drug Repositioning for the Discovery of New Anticancer Drugs[J]. International Journal of Biological Sciences, 2014, 10(7): 654-663. doi: 10.7150/ijbs.9224
[47]	阮小芸, 廖健斌, 李祥, 等. 基于人才知识图谱推理的强化学习可解释推荐研究[J]. 数据分析与知识发现, 2021, 5(6): 36-50.
[47]	( Ruan Xiaoyun, Liao Jianbin, Li Xiang, et al. Interpretable Recommendation of Reinforcement Learning Based on Talent Knowledge Graph Reasoning[J]. Data Analysis and Knowledge Discovery, 2021, 5(6): 36-50.)
[48]	Hristovski D, Stare J, Peterlin B, et al. Supporting Discovery in Medicine by Association Rule Mining in Medline and UMLS[J]. Studies in Health Technology and Informatics, 2001, 84(Pt 2): 1344-1348. pmid: 11604946
[49]	Henry S, McInnes B T. Indirect Association and Ranking Hypotheses for Literature Based Discovery[J]. BMC Bioinformatics, 2019, 20(1): 425. doi: 10.1186/s12859-019-2989-9 pmid: 31416434
[50]	赵明珍, 林鸿飞, 徐博, 等. 面向社交网络的潜在药物不良反应发现[J]. 中文信息学报, 2017, 31(5): 194-202.
[50]	( Zhao Mingzhen, Lin Hongfei, Xu Bo, et al. Potential Adverse Drug Reactions Discovery from Social Networks[J]. Journal of Chinese Information Processing, 2017, 31(5): 194-202.)
[51]	Imanishi T, Abe Y, Suto R, et al. Expression of the Human Multidrug Resistance Gene(MDR1) and Prognostic Correlation in Human Osteogenic Sarcoma[J]. The Tokai Journal of Experimental and Clinical Medicine, 1994, 19(1-2): 39-46. pmid: 7660382
[52]	Ye S N, Zhang J M, Shen J, et al. NVP-TAE 684 Reverses Multidrug Resistance(MDR) in Human Osteosarcoma by Inhibiting P-Glycoprotein(PGP1) Function[J]. British Journal of Pharmacology, 2016, 173(3): 613-626. doi: 10.1111/bph.13395
[53]	Lam A, Hoang J D, Singleton A, et al. Itraconazole and Clarithromycin Inhibit P-Glycoprotein Activity in Primary Human Sinonasal Epithelial Cells[J]. International Forum of Allergy & Rhinology, 2015, 5(6): 477-480.
[54]	Tsubamoto H, Sonoda T, Ikuta S, et al. Combination Chemotherapy with Itraconazole for Treating Metastatic Pancreatic Cancer in the Second-Line or Additional Setting[J]. Anticancer Research, 2015, 35(7): 4191-4196. pmid: 26124377
[55]	Tsubamoto H, Sonoda T, Yamasaki M, et al. Impact of Combination Chemotherapy with Itraconazole on Survival of Patients with Refractory Ovarian Cancer[J]. Anticancer Research, 2014, 34(5): 2481-2487. pmid: 24778064
[56]	Xu Y, Wang Q, Li X Z, et al. Itraconazole Attenuates the Stemness of Nasopharyngeal Carcinoma Cells via Triggering Ferroptosis[J]. Environmental Toxicology, 2021, 36(2): 257-266. doi: 10.1002/tox.23031

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