Mondo: integrating disease terminology across communities
Nicole Vasilevsky, Sabrina Toro, Nicolas Matentzoglu, Joseph E Flack, Kathleen R. Mullen, Harshad Hegde, Sarah Gehrke, Patricia L. Whetzel, Yousif J. Shwetar, Nomi L. Harris, Mee S. Ngu, Gioconda L Alyea, Megan Kane, Paola Roncaglia, Eric Sid, Courtney Thaxton, Valerie Wood, Roshini S. Abraham, Maria Isabel Achatz, Pamela Ajuyah, Joanna Amberger, Lawrence Babb, Jasmine Baker, James P. Balhoff, Jonathan S. Berg, Amol Bhalla, Xavier Bofill‐De Ros, Ian Braun, Eleanor C Broeren, Blake K Byer, Alicia B. Byrne, Tiffany J. Callahan, Leigh Carmody, Lauren Chan, Amanda Clause, Julie S. Cohen, Marcello DeLuca, Natalie Deuitch, May Flowers, Jamie L. Fraser, Toyofumi Fujiwara, Vanessa Gitau, Jennifer Goldstein, Dylan Gration, Tudor Groza, Benjamin M. Gyori, William Hankey, Jason A. Hilton, Daniel Himmelstein, Stephanie Hong, Charles Tapley Hoyt, Robert Huether, Eric Hurwitz, Julius O.B. Jacobsen, Atsuo Kikuchi, Sebastian Köhler, Daniel Korn, David Lagorce, Bryan Laraway, Jane Y Li, Adriana J Malheiro, James Alastair McLaughlin, Birgit Meldal, Shruthi Mohan, Sierra Moxon, Mónica Muñoz-Torres, Tristan Nelson, Frank W Nicholas, David Ochoa, Daniel Olson, Tudor I. Oprea, Tomiko Oskotsky, David Osumi-Sutherland, Kelley Paris, Helen Parkinson, Zoë May Pendlington, Xiao Peng, Amy Pizzino, Sharon E. Plon, Bradford C. Powell, Julie Ratliff, Heidi L. Rehm, Lyubov Remennik, Erin Rooney Riggs, Seán G. Roberts, Peter N. Robinson, Justyne Ross, Kevin Schaper, Brian M. Schilder, Johanna Schmidt, Elliott W Sharp, Morgan Similuk, Damian Smedley, Tam P. Sneddon, Rachel Sparks, Ray Stefancsik, Gregory S. Stupp, Shilpa Sundar, Terue Takatsuki, Imke Tammen
Abstract
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.