LncRNA与疾病关系的知识图谱构建

doi:10.6040/j.issn.1672-3961.0.2020.212

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.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 Lejun^1,2, YANG Lu¹, GAO Zhihong³, LI Huakang^1,4,5

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/Abstract

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

龚乐君,杨璐,高志宏,李华康. LncRNA与疾病关系的知识图谱构建[J]. 山东大学学报 (工学版), 2021, 51(2): 26-33.

GONG Lejun, YANG Lu, GAO Zhihong, LI Huakang. Construction of knowledge graph of relationship between LncRNA and diseases[J]. Journal of Shandong University(Engineering Science), 2021, 51(2): 26-33.

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LncRNA与疾病关系的知识图谱构建

Construction of knowledge graph of relationship between LncRNA and diseases

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