The Global Burden of Animal Diseases (GBADs) programme aims to assess the impact of animal health on agricultural animals, livestock production systems and associated communities worldwide. As part of the objectives of GBADs\'Animal Health Ontology theme, the programme reviewed conceptual frameworks, ontologies and classification systems in biomedical science. The focus was on data requirements in animal health and the connections between animal health and human and environmental health. In May 2023, the team conducted searches of recognised repositories of biomedical ontologies, including BioPortal, Open Biological and Biomedical Ontology Foundry, and Ontology Lookup Service, to identify animal and livestock ontologies and those containing relevant concepts. Sixteen ontologies were found, covering topics such as surveillance, anatomy and genetics. Notable examples include the Animal Trait Ontology for Livestock, the Animal Health Surveillance Ontology, the National Center for Biotechnology Information Taxonomy and the Uberon Multi-Species Anatomy Ontology. However, some ontologies lacked class definitions for a significant portion of their classes. The review highlights the need for domain evidence to support proposed models, critical appraisal of external ontologies before reuse, and external expert reviews along with statistical tests of agreements. The findings from this review informed the structural framework, concepts and rationales of the animal health ontology for GBADs. This animal health ontology aims to increase the interoperability and transparency of GBADs data, thereby enabling estimates of the impacts of animal diseases on agriculture, livestock production systems and associated communities globally.
Le programme \" Impact mondial des maladies animales \" (GBADs) vise à évaluer l\'impact de la santé animale sur les animaux d\'élevage, les systèmes de production animale et les communautés liées à ce secteur d\'activités dans le monde. Afin de définir une ontologie de la santé animale répondant aux objectifs du GBADs, le programme a procédé à un examen des cadres conceptuels, des ontologies et des systèmes de classification actuellement appliqués en sciences biomédicales. Il s\'agissait de définir les besoins en données dans le domaine de la santé animale ainsi que les connexions entre la santé animale, la santé publique et la santé environnementale. En mai 2023, l\'équipe a procédé à des recherches dans des référentiels reconnus d\'ontologies biomédicales, notamment BioPortal, Open Biological and Biomedical Ontology Foundry et Ontology Lookup Service, afin de recenser les ontologies relatives aux animaux et au bétail ainsi que celles contenant des concepts pertinents. Seize ontologies ont été relevées, couvrant des thèmes tels que la surveillance, l\'anatomie et la génétique. Parmi les exemples notables on peut citer : Animal Trait Ontology for Livestock (ontologie dédiée aux caractères phénotypiques des animaux d\'élevage), Animal Health Surveillance Ontology (ontologie dédiée à la surveillance de la santé animale), National Center for Biotechnology Information Taxonomy (la base de données Taxonomie du Centre américain pour les informations biotechnologiques), et Uberon Multi-Species Anatomy Ontology (ontologie anatomique représentant diverses espèces animales). Il a cependant été constaté que certaines ontologies ne disposent pas de définitions de classes pour une grande partie des classes qui les composent. L\'examen a souligné l\'importance d\'étayer les modèles proposés par des données issues des spécialités en question, de procéder à une évaluation critique des ontologies externes avant de les réutiliser et de faire effectuer des examens complémentaires par des experts externes ainsi que des tests statistiques de concordance. Les résultats de cette étude ont apporté des éléments permettant de définir le cadre structurel, les concepts et les principes de l\'ontologie relative à la santé animale destinée au GBADs. Cette ontologie de la santé animale vise à accroître l\'interopérabilité et la transparence des données du GBADs, ce qui permet d\'effectuer des estimations de l\'impact des maladies animales sur l\'agriculture, les systèmes de production animale et les communautés associées à ce secteur d\'activités à l\'échelle mondiale.
El programa sobre el impacto global de las enfermedades animales (GBADs) tiene como objetivo evaluar el impacto de la sanidad animal en los animales de granja, los sistemas de producción ganadera y las comunidades conexas en todo el mundo. Como parte de los objetivos en torno al tema de la ontología de la sanidad animal del GBADs, el programa revisó marcos conceptuales, ontologías y sistemas de clasificación en el ámbito de la ciencia biomédica. Se hizo hincapié en los requisitos de datos sobre la sanidad animal y en las conexiones entre la sanidad animal y la salud humana y ambiental. En mayo de 2023, el equipo realizó búsquedas en repositorios reconocidos de ontologías biomédicas, como BioPortal, Open Biological and Biomedical Ontology Foundry y Ontology Lookup Service, para identificar no solo ontologías animales y ganaderas, sino también aquellas que incluyeran conceptos relevantes. En este sentido, se encontraron dieciséis ontologías, que abarcan temas como vigilancia, anatomía y genética. Entre los ejemplos más destacados figuran Animal Trait Ontology for Livestock (Ontología de Características Animales para el Ganado), Animal Health Surveillance Ontology (Ontología de Vigilancia de la Sanidad Animal), National Center for Biotechnology Information Taxonomy (la base de datos Taxonomía del Centro Nacional para la Información Biotecnológica) y Uberon Multi-Species Anatomy Ontology (Ontología Anatómica de Especies Múltiples). Sin embargo, algunas ontologías carecían de definiciones para una parte significativa de sus clases. La revisión pone de relieve la necesidad de contar con datos probatorios del ámbito en cuestión que respalden los modelos propuestos, una evaluación crítica de las ontologías externas antes de su reutilización y revisiones de expertos externos junto con pruebas estadísticas de los acuerdos. Los resultados de esta revisión han servido de base para el marco estructural, los conceptos y los fundamentos de la ontología de la sanidad animal para el GBADs. Esta ontología pretende aumentar la interoperabilidad y la transparencia de los datos del GBADs, permitiendo así estimar el impacto de las enfermedades animales en la agricultura, los sistemas de producción ganadera y las comunidades conexas en todo el mundo.

Ontologies and terminologies serve as the backbone of knowledge representation in biomedical domains, facilitating data integration, interoperability, and semantic understanding across diverse applications. However, the quality assurance and enrichment of these resources remain an ongoing challenge due to the dynamic nature of biomedical knowledge. In this editorial, we provide an introductory summary of seven articles included in this special supplement issue for quality assurance and enrichment of biological and biomedical ontologies and terminologies. These articles span a spectrum of topics, such as development of automated quality assessment frameworks for Resource Description Framework (RDF) resources, identification of missing concepts in SNOMED CT through logical definitions, and developing a COVID interface terminology to enable automatic annotations of COVID-19 related Electronic Health Records (EHRs). Collectively, these contributions underscore the ongoing efforts to improve the accuracy, consistency, and interoperability of biomedical ontologies and terminologies, thus advancing their pivotal role in healthcare and biomedical research.

Automatic disease progression prediction models require large amounts of training data, which are seldom available, especially when it comes to rare diseases. A possible solution is to integrate data from different medical centres. Nevertheless, various centres often follow diverse data collection procedures and assign different semantics to collected data. Ontologies, used as schemas for interoperable knowledge bases, represent a state-of-the-art solution to homologate the semantics and foster data integration from various sources. This work presents the BrainTeaser Ontology (BTO), an ontology that models the clinical data associated with two brain-related rare diseases (ALS and MS) in a comprehensive and modular manner. BTO assists in organizing and standardizing the data collected during patient follow-up. It was created by harmonizing schemas currently used by multiple medical centers into a common ontology, following a bottom-up approach. As a result, BTO effectively addresses the practical data collection needs of various real-world situations and promotes data portability and interoperability. BTO captures various clinical occurrences, such as disease onset, symptoms, diagnostic and therapeutic procedures, and relapses, using an event-based approach. Developed in collaboration with medical partners and domain experts, BTO offers a holistic view of ALS and MS for supporting the representation of retrospective and prospective data. Furthermore, BTO adheres to Open Science and FAIR (Findable, Accessible, Interoperable, and Reusable) principles, making it a reliable framework for developing predictive tools to aid in medical decision-making and patient care. Although BTO is designed for ALS and MS, its modular structure makes it easily extendable to other brain-related diseases, showcasing its potential for broader applicability.Database URL  https://zenodo.org/records/7886998 .

Ontologies play a key role in representing and structuring domain knowledge. In the biomedical domain, the need for this type of representation is crucial for structuring, coding, and retrieving data. However, available ontologies do not encompass all the relevant concepts and relationships. In this paper, we propose the framework SiMHOMer (Siamese Models for Health Ontologies Merging) to semantically merge and integrate the most relevant ontologies in the healthcare domain, with a first focus on diseases, symptoms, drugs, and adverse events. We propose to rely on the siamese neural models we developed and trained on biomedical data, BioSTransformers, to identify new relevant relations between concepts and to create new semantic relations, the objective being to build a new merging ontology that could be used in applications. To validate the proposed approach and the new relations, we relied on the UMLS Metathesaurus and the Semantic Network. Our first results show promising improvements for future research.

Biomedical data analysis and visualization often demand data experts for each unique health event. There is a clear lack of automatic tools for semantic visualization of the spread of health risks through biomedical data. Illnesses such as coronavirus disease (COVID-19) and Monkeypox spread rampantly around the world before governments could make decisions based on the analysis of such data. We propose the design of a knowledge graph (KG) for spatio-temporal tracking of public health event propagation. To achieve this, we propose the specialization of the Core Propagation Phenomenon Ontology (PropaPhen) into a health-related propagation phenomenon domain ontology. Data from the UMLS and OpenStreetMaps are suggested for instantiating the proposed knowledge graph. Finally, the results of a use case on COVID-19 data from the World Health Organization are analyzed to evaluate the possibilities of our approach.

The population of dementia patients is on the rise, as society undergoes rapid aging. This led to an expansion of dementia-related data. This study aims to develop a comprehensive dementia ontology to facilitate the collection and analysis of high-quality dementia data. We followed an ontology building process from Ontology Development 101 and the content of the dementia ontology was validated by experts in dementia care and dementia-related research.

Common Data Models (CDMs) enhance data exchange and integration across diverse sources, preserving semantics and context. Transforming local data into CDMs is typically cumbersome and resource-intensive, with limited reusability. This article compares OntoBridge, an ontology-based tool designed to streamline the conversion of local datasets into CDMs, with traditional ETL methods in adopting the OMOP CDM. We examine flexibility and scalability in the management of new data sources, CDM updates, and the adoption of new CDMs. OntoBridge showed greater flexibility in integrating new data sources and adapting to CDM updates. It was also more scalable, facilitating the adoption of various CDMs like i2b2, unlike traditional methods reliant on OMOP-specific tools developed by OHDSI. In summary, while traditional ETL provides a structured approach to data integration, OntoBridge offers a more flexible, scalable, and maintenance-efficient alternative.

The task of managing diverse electronic health records requires the consolidation of data from different sources to facilitate clinical research and decision-making support, with the emergence of the Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) as a standard relational database schema for structuring health records from different sources. Working with ontologies is strongly associated with reasoners. Implementing them over expansive and intricate Ontologies can pose computational challenges, potentially resulting in slow performance. In this paper, we propose the implementation of a new reasoner based on categorical logic over a translation of OMOP-CDM into an ontology model. This enables enhancements to the efficiency and scalability of implementing such models.

Interoperability is crucial to overcoming various challenges of data integration in the healthcare domain. While OMOP and FHIR data standards handle syntactic heterogeneity among heterogeneous data sources, ontologies support semantic interoperability to overcome the complexity and disparity of healthcare data. This study proposes an ontological approach in the context of the EUCAIM project to support semantic interoperability among distributed big data repositories that have applied heterogeneous cancer image data models using a semantically well-founded Hyperontology for the oncology domain.

Ontology is essential for achieving health information and information technology application interoperability in the biomedical fields and beyond. Traditionally, ontology construction is carried out manually by human domain experts (HDE). Here, we explore an active learning approach to automatically identify candidate terms from publications, with manual verification later as a part of a deep learning model training and learning process. We introduce the overall architecture of the active learning pipeline and present some preliminary results. This work is a critical and complementary component in addition to manually building the ontology, especially during the long-term maintenance stage.