您的位置:山东大学 -> 科技期刊社 -> 《山东大学学报(工学版)》

山东大学学报 (工学版) ›› 2021, Vol. 51 ›› Issue (2): 26-33.doi: 10.6040/j.issn.1672-3961.0.2020.212

• • 上一篇    

LncRNA与疾病关系的知识图谱构建

龚乐君1,2,杨璐1,高志宏3,李华康1,4,5   

  1. 1.南京邮电大学计算机学院、软件学院、网络空间安全学院, 江苏 南京 210023;2.江苏省大数据安全与智能处理重点实验室, 江苏 南京 210023;3.浙江省智慧医疗工程技术研究中心, 浙江 温州 325035;4.自然资源部城市国土资源监测与仿真重点实验室, 广东 深圳 518034;5.苏州派维斯信息科技有限公司, 江苏 苏州 215011
  • 发布日期:2021-04-16
  • 作者简介:龚乐君(1978— ),女,江西临川人,博士,副教授,硕士生导师,主要研究方向为数据与文本挖掘,生物医学信息处理. E-mail:glj98226@163.com
  • 基金资助:
    国家自然科学基金资助项目(61502243);浙江省智慧医疗工程技术研究中心资助项目(2016E10011);中国博士后基金资助项目(2018M632349);江苏省“六大人才高峰”高层次人才项目(XYDXX-204);自然资源部城市国土资源监测与仿真重点实验室开放基金资助课题(KF-2019-04-011,KF-2019-04-065);苏州市姑苏科技创业天使计划项目(CYTS2018233)资助;南京邮电大学引进人才科研启动基金资助(NY217136)

Construction of knowledge graph of relationship between LncRNA and diseases

GONG Lejun1,2, YANG Lu1, GAO Zhihong3, LI Huakang1,4,5   

  1. 1. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, Jiangsu, China;
    2. Jiangsu Key Lab of Big Data Security &
    Intelligent Processing, Nanjing 210023, Jiangsu, China;
    3. Zhejiang Engineering Research Center of Intelligent Medicine, Wenzhou 325035, Zhejiang, China;
    4. Key Laboratory of Urban Land Resources Monitoring and Simulation, Shenzhen 518034, Guangdong, China;
    5. Suzhou Privacy Information Technology Company, Suzhou 215011, Jiangsu, China
  • Published:2021-04-16

摘要: 基于长链非编码核糖核酸(long non-coding RNA, LncRNA)和疾病关系的分析,对LncRNA和疾病知识概念建模,提出一种有效的LncRNA与疾病关系的知识图谱构建方法。使用Protégé构建本体结构,建构概念层,整合结构化与非结构化两种不同来源的数据形成数据层,通过资源描述框架(resource description frame, RDF)/网络本体语言(web ontology language, OWL)技术对数据及相应的关系进行描述,采用基于前向推理的产生式规则进行相应的知识推理,通过SPARQL查询语言和可视化技术展示知识查询的推理效果。这一研究将为LncRNA与疾病的关系研究提供参考,推动该领域的发展。

关键词: LncRNA, 知识表示, LncRNA-疾病关联, 知识图谱, 本体

Abstract: Based on the analysis of the relationship between LncRNA and diseases, the concept of knowledge modeling of LncRNA and diseases was proposed, and an effective method of knowledge mapping of the relationship between LncRNA and diseases was proposed. Protege was used to construct the ontology structure and the concept layer, integrate the structured and unstructured data from two different sources to the data layer, and describe the data and the corresponding relationship through RDF/OWL technology. The production rules based on forward reasoning were used to carry out the corresponding knowledge reasoning. The inference effect of knowledge query was demonstrated by SPARQL query language and visualization technology. This study provided further reference value for the study of the relationship between LncRNA and diseases. Moreover, it also promoted the development of this field.

Key words: LncRNA, knowledge representation, LncRNA-disease association, knowledge graph, ontology

中图分类号: 

  • TP391
[1] CLACK M B, MATTICK J S. Long noncoding RNAs in cell biology[J]. Seminars in Cell & Developmental Biology, 2011, 22(4):366-376.
[2] LOURO R, SMIRNOVA A S, VERNOVSKI-ALMEIDA S. Long intronic noncoding RNA transcription: expression noise or expression choice?[J]. Genomics, 2009, 93(4):291-298.
[3] GEISLER S, COLLER J. RNA in unepected places:long non-coding RNA functions in diverse cellular contexts[J]. Nature Reviews Molecular Cell Biology, 2013, 14(11):699-712.
[4] CHEN X, YAN C C, LUO C, et al. Constructing lncRNA functional similarity netword based on lncRNA-disease associations and disease semantic similarity[J]. Scientific Reports, 2015, 5:11338.
[5] SUN J, SHI H B, WANG Z Z, et al. Inferring novel lncRNA-disease associations based on a random walk model of a lncRNA functional similarity network[J]. Molecular Biosystems, 2014, 10(8):2074-2081.
[6] 周众. LncRNA-基因调控关系的生物信息学数据库构建及分析预测[D].合肥:中国科学技术大学,2016. ZHOU Zhong. Bioinformatics database construction, analysis and prediction of LncRNA-gene regulatory relation-ships[D]. Hefei:University of Science and Technology of China, 2016.
[7] 朱木易洁,鲍秉坤,徐常胜. 知识图谱发展与构建的研究进展[J]. 南京信息工程大学学报(自然科学版),2017,9(6):575-582. ZHU Muyijie, BAO Bingkun, XU Changsheng. Research progress on development and construction of knowledge graph[J]. J Nanjing Univ Inf Sci Technol, 2017, 9(6):575-582.
[8] LIU Q, LI Y, DUAN H, et al. Knowledge graph construction techniques[J]. Journal of Computer Research and Development, 2016, 53(3): 582-600.
[9] MENG X F, DU Z J. Research on the big data fusion: issues and challenges[J]. Journal of Computer Research and Development, 2016, 53(2):231-246.
[10] DUAN Y, SHAO L, HU G. Specifying knowledge graph with data graph, information graph, knowledge graph, and wisdom graph[J]. International Journal of Software Innovation, 2018, 6(2):10-25.
[11] ZELENKO D, AONE C, RICHARDELLA A. Kernel methods for relation extraction[J]. Journal of Machine Learning Research, 2003, 3:1083-1106.
[12] WANG J,WANG Z, ZHANG D, et al. Combining knowledge with deep convolutional neual Nnetworks for short text classification[C] //Proc of the Twenty-Sixth International Joint Conference on Artifical Intelligence. Melbourne, Australia: Sierra C, 2017:2915-2921.
[13] LIU M X, CHEN X, CHEN G, et al. A computational framework to infer human disease-ass-ociated long noncoding RNAs[J]. PLoS One, 2014, 9(1):e84408.
[14] CHEN X. Predicting lncRNA-disease associateions and constructing lncRNA functional similarity network based on the information of miRNA[J]. Scientific Reports, 2015, 5:13186.
[15] MORRAN V A, PERERA R J, KHALIL A M. Emerging functional and mechanistic paradigms of mammalian long non-coding RNAs[J]. Nucleic Acids Res, 2012, 40(14):6391-6400.
[16] WISHART D S, KNOX C, SHRIVASTAVA S, et al. A knowledgebase for drugs, drug actions and drug targets[J]. Nucleic Acids Res, 2008, 36(Database issue):D901-6.
[17] VOLDERS P J, HELSENS K, WANG X, et al. LNCipedia: a database for annotated human lncRNA tran-script sequences and structures[J]. Nucleic Acids Research, 2013, 41(Database issue):D246-D251.
[18] LYNN S, ARZE C, NADENDLA S, et al. Disease ontology: a backbone for disease semantic integration[J]. Nucleic Acids Res, 2012, 40(Database issue):D940-D946.
[19] CHEN G, WANG Z, WANG D, et al. LncRNA disease: a database for long-noncoding RNA-associated diseases[J]. Nucleic Acids Res, 2013, 41(D1):D983-986.
[20] BAO Z, YANG Z, HUANG Z, et al. LncRNA Disease 2.0: an updated database of long non-coding RNA-associated diseases[J]. Nucleic Acids Res, 2019, 47(D1):D1034-D1037.
[21] 杨霄月, 李建伟. LncRNA调控人类疾病关系数据库的研究[J]. 医学信息, 2019, 32(12):28-30. YANG Xiaoyue, LI Jianwei. LncRNA regulation of human disease relationship database[J]. Medical Information, 2019, 32(12):28-30.
[22] OLDAKOWSKI R. D2RQ platform-treating non-RDF databases as virtual RDF graphs[J/OL]. Nat Prec, 2011. https://doi.org/10.1038/npre.2011.5660.1
[1] 段江丽,胡新. 自然语言问答中的语义关系识别[J]. 山东大学学报 (工学版), 2020, 50(3): 1-7.
[2] 苏佳林,王元卓,靳小龙,程学旗. 自适应属性选择的实体对齐方法[J]. 山东大学学报 (工学版), 2020, 50(1): 14-20.
[3] 孔令龙,田国会. 智能家庭中一种基于本体的机器人服务认知机制[J]. 山东大学学报 (工学版), 2019, 49(6): 45-54.
[4] 崔晓松,王颖,孟佳, 邹丽. 基于语言值相似度推理的网络商家自评价方法[J]. 山东大学学报(工学版), 2018, 48(1): 1-7.
[5] 李庆冬,骆伟超,叶瑛歆,张承瑞,胡天亮. 基于本体的机床设备资源共享机制[J]. 山东大学学报(工学版), 2017, 47(3): 130-138.
[6] 刘东慧1,2,姜薇1*. 基于事件本体的Web不良信息挖掘[J]. 山东大学学报(工学版), 2012, 42(5): 35-40.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!