Zhan Chaoying, Ren Shumin, Zhang Yuxin, Lv Xiaojun, Chen Yalan, Zheng Xin, Wu Rongrong, Wu Erman, Tang Tong, Wang Jiao, Bi Cheng, He Mengqiao, Liu Xingyun, Zhang Ke, Zhang Yingbo, Shen Bairong
Department of Cardiology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, Sichuan, China.
Information Center, Chengdu Second People's Hospital, The Affiliated Hospital of Sichuan University, Chengdu, 610072, Sichuan, China.
Comput Biol Med. 2025 May;190:110107. doi: 10.1016/j.compbiomed.2025.110107. Epub 2025 Apr 1.
As biotechnology and computer science continue to advance, there's a growing amount of biomedical data worldwide. However, standardizing and consolidating these data remains challenging, making analysis and comprehension more difficult. To enhance research on complex diseases like myocardial infarction (MI), an ontology is necessary to ensure consistent data labeling and knowledge representation. This will facilitate data management and the application of artificial intelligence techniques in this field, ultimately advancing precision medicine research for MI. This study introduced the MI Ontology (MIO), which was developed using Stanford's seven-step method and Protégé. MIO aims to support precision medicine research on MI by effectively modeling and representing MI-related concepts and relationships. The validation of the MIO model involved employing Ontology Web Language (OWL) reasoners and comparing it with other disease-specific ontologies. MIO is an ontology model comprising of 3090 classes, 14 object attributes, 3494 individuals, 9415 synonyms and 49263 axioms, which encompass knowledge related to MI such as anatomical entities, clinical findings, drugs, genes, influencing factors, pathogenesis, patients-related concepts, procedures, and disease types. Furthermore, MIO has passed logical consistency validation and exhibits a broader conceptual scope and deeper knowledge structure than other disease-specific ontologies. Additionally, clinical use scenarios for MIO were developed to help address specific clinical problems. This study constructed the first comprehensive disease-specific ontology in cardiovascular diseases, named MIO, to promote precision medicine research on MI. MIO integrates and standardizes medical data, addressing complexity and standardization challenges. This promotes the use of big data analysis, explainable AI, and deep phenotype research in precision medicine. Future efforts will focus on enhancing and expanding MIO's applicability and scalability for superior services in this field.
随着生物技术和计算机科学的不断发展，全球生物医学数据量日益增长。然而，对这些数据进行标准化和整合仍然具有挑战性，这使得分析和理解变得更加困难。为了加强对心肌梗死（MI）等复杂疾病的研究，需要一种本体来确保数据标记和知识表示的一致性。这将促进数据管理以及人工智能技术在该领域的应用，最终推动MI的精准医学研究。本研究介绍了MI本体（MIO），它是使用斯坦福大学的七步法和Protégé开发的。MIO旨在通过有效地建模和表示与MI相关的概念和关系来支持MI的精准医学研究。MIO模型的验证包括使用本体网络语言（OWL）推理器并将其与其他特定疾病本体进行比较。MIO是一个本体模型，由3090个类、14个对象属性、3494个个体、9415个同义词和49263个公理组成，涵盖了与MI相关的知识，如解剖实体、临床发现、药物、基因、影响因素、发病机制、与患者相关的概念、程序和疾病类型。此外，MIO已经通过了逻辑一致性验证，并且与其他特定疾病本体相比，具有更广泛的概念范围和更深的知识结构。此外，还开发了MIO的临床使用场景，以帮助解决特定的临床问题。本研究构建了心血管疾病领域首个全面的特定疾病本体，名为MIO，以促进MI的精准医学研究。MIO整合并标准化医学数据，应对复杂性和标准化挑战。这促进了大数据分析、可解释人工智能和精准医学中深度表型研究的应用。未来将致力于增强和扩展MIO在该领域的适用性和可扩展性，以提供更优质的服务。
