Publications by authors named "Nicolas A Matentzoglu"
The Vertebrate Breed Ontology: Toward Effective Breed Data Standardization.
J Vet Intern Med
May 2025
Background: Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison; this ultimately impedes the application of existing information-based tools to support advancement in diagnostics, treatments, and precision medicine.
Hypothesis/objectives: A single, coherent, logic-based standard for documenting breed names in health, production, and research-related records will improve data use capabilities in veterinary and comparative medicine.
Animals: No live animals were used.
Background –: Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison; this ultimately impedes the application of existing information-based tools to support advancement in diagnostics, treatments, and precision medicine.
Hypothesis/objectives –: A single, coherent, logic-based standard for documenting breed names in health, production, and research-related records will improve data use capabilities in veterinary and comparative medicine.
Animals –: No live animals were used.
Article Synopsis
- Translational research needs data from different levels of biological systems, but combining that data is tough for scientists.
- New technologies help gather more data, but researchers struggle to organize all the information effectively.
- PheKnowLator is a tool that helps scientists create customizable knowledge graphs easily, making it better for managing complex health information without slowing down their work.
Background: Navigating the clinical literature to determine the optimal clinical management for rare diseases presents significant challenges. We introduce the Medical Action Ontology (MAxO), an ontology specifically designed to organize medical procedures, therapies, and interventions.
Methods: MAxO incorporates logical structures that link MAxO terms to numerous other ontologies within the OBO Foundry.
Navigating the vast landscape of clinical literature to find optimal treatments and management strategies can be a challenging task, especially for rare diseases. To address this task, we introduce the Medical Action Ontology (MAxO), the first ontology specifically designed to organize medical procedures, therapies, and interventions in a structured way. Currently, MAxO contains 1757 medical action terms added through a combination of manual and semi-automated processes.
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- Common data models standardize electronic health record (EHR) data but struggle to fully integrate the necessary resources for deep phenotyping.
- The OMOP2OBO algorithm automates the mapping of Observational Medical Outcomes Partnership (OMOP) vocabularies to Open Biological and Biomedical Ontology (OBO) ontologies, significantly reducing the need for manual curation.
- With OMOP2OBO, mappings for a large number of conditions, drugs, and measurements were created, facilitating the identification of undiagnosed patients in rare diseases and enhancing opportunities for EHR-based deep phenotyping.