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.

本体是知识领域内概念（或类）、属性以及概念之间关系的形式表示。本体论和术语在生物医学研究中发挥着至关重要的作用，用于编码、管理、共享和交换不断生成的大量异构生物医学数据，例如电子健康记录 (EHR)。 EHR 已广泛用于转化研究，以学习不同患者群体的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群体识别。群组识别涉及资格标准集合的规范，需要使用 EHR 的语义主干（即编码系统或本体）将其转换为可计算的表示，然后才能针对 EHR 数据库运行查询。然而，从大规模电子病历中进行有效的队列识别存在两个关键障碍。第一个是由编码系统的混合使用引起的数据（或语义）异质性。第二个是语义主干或本体层次结构的质量，这对于将患者资格标准转化为可执行的数据库查询至关重要。为了应对这些挑战，该项目将开发本体匹配和本体质量增强的新方法，直接影响生物医学中的数据科学实践，例如患者队列识别。此外，该项目将把提出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 将开发新的基于图神经网络 (GNN) 的学习方法，通过利用统一医学语言系统等来源中嵌入的知识来匹配生物医学本体。这将解决异构性问题并实现语义互操作性。在目标 2 中，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实现本体层次结构的持续增强。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结果匹配概念和检测到的质量问题。 PI 将向各自的本体所有者传达经过验证的质量问题，以便在后续本体版本中进行纠正。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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

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

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

Identifying Missing IS-A Relations in Orphanet Rare Disease Ontology

识别孤儿罕见疾病本体中缺失的 IS-A 关系

• DOI: 10.1109/bibm55620.2022.9995614
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM
• 作者: Mohtashamian, Maryamsadat;Abeysinghe, Rashmie;Hao, Xubing;Cui, Licong
• 通讯作者: Cui, Licong

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.