Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities⁠-including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology-a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene-disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.

Interoperability between clinical datasets is challenging due to, in part, the number of data models and vocabularies in use and the variety of implementations. Here we describe the first steps in an ongoing effort to achieve interoperability between two clinical datasets currently being constructed within independent international projects. Both are utilizing the FAIR Principles but have constructed their data models independently and have selected different ontologies. In this initial exploratory experiment, we examined the degree to which a mapping of both models into an independent schema, Biolink, can increase interoperability. Mapping was achieved by categorizing the key nodes in both data models as “types” of concepts in the Biolink schema. We found that with this very thin mapping in place, and without changing either model, queries could be constructed that extracted data from both datasets, demonstrating that at least some degree of interoperability had been achieved. Our results support the use of FAIR-compliant data representations, which are, by nature, more interoperable than legacy clinical data representations, even when the models have not been coordinated upfront.

Interoperability between clinical datasets is challenging due to, in part, the number of data models and vocabularies in use and the variety of implementations. Here we describe the first steps in an ongoing effort to achieve interoperability between two clinical datasets currently being constructed within independent international projects. Both are utilizing the FAIR Principles but have constructed their data models independently and have selected different ontologies. In this initial exploratory experiment, we examined the degree to which a mapping of both models into an independent schema, Biolink, can increase interoperability. Mapping was achieved by categorizing the key nodes in both data models as “types” of concepts in the Biolink schema. We found that with this very thin mapping in place, and without changing either model, queries could be constructed that extracted data from both datasets, demonstrating that at least some degree of interoperability had been achieved. Our results support the use of FAIR-compliant data representations, which are, by nature, more interoperable than legacy clinical data representations, even when the models have not been coordinated upfront.