CAREER: Advancing the Role of Ontologies for Data Science in Biomedicine

职业：推进数据科学本体在生物医学中的作用

• 批准号: 2047001
• 负责人: Licong Cui
• 金额: $ 53.35万
• 依托单位: The University of Texas Health Science Center at Houston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047001&HistoricalAwards=false
• 关键词: CAREER; Advancing; Role; Ontologies; Data

An ontology is a formal representation of concepts (or classes), properties, and relationships between concepts within a knowledge domain. Ontologies and terminologies have played a vital role in biomedical research for coding, managing, sharing, and exchange of vast amounts of heterogeneous biomedical data that are being continuously generated, such as in Electronic Health Records (EHRs). EHRs have been widely used in translational research to learn predictive models for discovery and disease management across varying patient cohorts. The very first step in such EHR-based applications often concerns patient cohort identification. Cohort identification involves the specification of a collection of eligibility criterion that needs to be transformed into a computable representation using the EHR’s semantic backbone (i.e., coding systems or ontologies) before queries can run against the EHR database. However, there are two critical barriers in performing effective cohort identification from large-scale EHRs. The first one is data (or semantic) heterogeneity, caused by a mixed utilization of coding systems. The second one is the quality of the semantic backbone or ontology hierarchy, which is essential for translating patient eligibility criteria to executable database queries. To address such challenges, this project will develop new methods for ontology matching and for ontology quality enhancement that directly impact data science practice in biomedicine, such as patient cohort identification. In addition, this project will incorporate the proposed computational aspects into data science-based courses to train next generation data scientists.This project consists of three research objectives. In Objective 1, the PI will develop new graph neural network (GNN)-based learning methods for matching biomedical ontologies by harnessing knowledge embedded in sources such as the Unified Medical Language System. This will address the heterogeneity issue and achieve semantic interoperability. In Objective 2, the PI will develop learning-based methods for detecting quality defects in subclass relations. This will address the quality issue and achieve continued enhancement of ontology hierarchies. In Objective 3, the PI will develop an ontology-based COVID-19 query engine for patient cohort identification, which is a real-world application of enhancing semantic interoperability for supporting data-driven COVID-19 research. For evaluation of the proposed methods, domain experts will be involved in validation of the resulted matching concepts and detected quality issues. The PI will communicate validated quality issues to the respective ontology owners for correction in subsequent ontology versions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

本体论是知识领域内概念（或类），属性和概念之间的关系的正式表示。本体和术语在生物医学研究中起着至关重要的作用，用于编码，管理，共享和交换大量的异质生物医学数据，这些数据是不断生成的，例如电子健康记录（EHRS）。 EHR已被广泛用于翻译研究中，以学习各种患者队列中发现和疾病管理的预测模型。此类基于EHR的应用程序的第一步通常涉及患者队列的识别。队列识别涉及规范合格标准的集合，该标准需要使用EHR的语义主链（即编码系统或本体论）转换为可计算的表示，然后在查询与EHR数据库进行查询之前。但是，从大规模EHR进行有效的队列鉴定时有两个关键障碍。第一个是由编码系统混合利用引起的数据（或语义）异质性。第二个是语义主链或本体论层次结构的质量，这对于将患者的可用性标准转换为可执行数据库查询至关重要。为了应对此类挑战，该项目将开发新的本体匹配和本体质量增强方法，这些方法直接影响生物医学的数据科学实践，例如患者队列鉴定。此外，该项目将将拟议的计算方面纳入基于数据科学的课程中，以培训下一代数据科学家。该项目由三个研究目标组成。在目标1中，PI将通过利用嵌入在统一的医学语言系统之类的来源中的知识来开发新的图形神经网络（GNN）的学习方法，以匹配生物医学本体。这将在目标2中解决，PI将开发基于学习的方法来检测子类关系中的质量缺陷。这将解决质量问题，并持续增强本体论等级制度。在目标3中，PI将开发基于本体的COVID-19查询引擎，用于患者队列识别，这是增强语义互操作性以支持数据驱动的Covid-19研究的现实应用。为了评估所提出的方法，领域专家将参与验证所得的匹配概念和检测到的质量问题。 PI将在随后的本体论版本中将经过验证的质量问题传达给各自的本体所有者以进行更正。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，通过评估被认为是珍贵的支持。

项目成果

A Query Engine for Self-controlled Case Series: with an application to COVID-19 EHR data

用于自我控制案例系列的查询引擎：适用于 COVID-19 EHR 数据

• 发表时间: 2023
• 期刊: AMIA Summits on Translational Science Proceedings
• 作者: Li, Xiaojin;Huang, Yan;Cui, Licong;Zhang, Guo-Qiang
• 通讯作者: Zhang, Guo-Qiang

A UMLS-based Investigation of Laterality in Biomedical Terminologies

基于 UMLS 的生物医学术语偏侧性研究

• 发表时间: 2023
• 期刊: AMIA Jt Summits Transl Sci Proc
• 作者: Abeysinghe, Rashmie;Hao, Xubing;Cui, Licong;Zhang, Guo-Qiang
• 通讯作者: Zhang, Guo-Qiang

A substring replacement approach for identifying missing IS-A relations in SNOMED CT

一种用于识别 SNOMED CT 中缺失 IS-A 关系的子串替换方法

• DOI: 10.1109/bibm55620.2022.9995595
• 发表时间: 2023
• 期刊: International Conference on Bioinformatics and Biomedicine
• 作者: Hao, Xubing;Abeysinghe, Rashmie;Shi, Jay;Cui, Licong
• 通讯作者: Cui, Licong

An evidence-based lexical pattern approach for quality assurance of Gene Ontology relations.

• DOI: 10.1093/bib/bbac122
• 发表时间: 2022-05-13
• 期刊: Briefings in bioinformatics
• 影响因子: 9.5

Automated Identification of Missing IS-A Relations in Human Phenotype Ontology

自动识别人类表型本体中缺失的 IS-A 关系

• 发表时间: 2022
• 期刊: AMIA Annual Symposium proceedings
• 作者: Mohtashamian, Maryamsadat;Hu, Ran;Abeysinghe, Rashmie;Hao, Xubing;Xu, Hua;Cui, Licong.
• 通讯作者: Cui, Licong.