Mondo: Unifying diseases for the world, by the world
Nicole Vasilevsky(University of Colorado Anschutz Medical Campus), Nicolas Matentzoglu, Sabrina Toro(University of Colorado Anschutz Medical Campus), Joseph E Flack(Johns Hopkins University), Harshad Hegde(Lawrence Berkeley National Laboratory), Deepak Unni(Lawrence Berkeley National Laboratory), Gioconda Alyea, Joanna Amberger(Johns Hopkins University), Lawrence Babb(Broad Institute), James P. Balhoff(Renaissance Computing Institute), Taylor I. Bingaman(Autism & Developmental Medicine Institute), Gully Burns(Chan Zuckerberg Initiative (United States)), Orion J. Buske, Tiffany J. Callahan(Columbia University), Leigh Carmody(Jackson Laboratory), Paula Carrio-Cordo(University of Zurich), Lauren Chan(Oregon State University), George S Chang(National Cancer Institute), S. Christiaens(Centerforce), Michel Dumontier, Laura Failla(National Institute of Allergy and Infectious Diseases), May J Flowers(Invitae (United States)), H. Alpha Garrett(National Cancer Institute), Jennifer Goldstein(University of North Carolina at Chapel Hill), Dylan Gration, Tudor Groza(European Bioinformatics Institute), Marc Hanauer(Inserm), Nomi L. Harris(Lawrence Berkeley National Laboratory), Jason A. Hilton(Stanford University), Daniel Himmelstein(Clinical Orthopaedics and Related Research), Charles Tapley Hoyt(Harvard University), Megan Kane(National Institutes of Health), Sebastian Köhler, David Lagorce(Inserm), Abbe Lai(Boston Children's Hospital), Martin Larralde, Antonia Lock(European Bioinformatics Institute), Irene López Santiago(Open Targets), Donna Maglott, Adriana J Malheiro, Birgit Meldal(European Bioinformatics Institute), Mónica Muñoz-Torres(University of Colorado Anschutz Medical Campus), Tristan Nelson, F. W. Nicholas(The University of Sydney), David Ochoa(European Bioinformatics Institute), Daniel Olson(Critical Path Institute), Tudor I. Oprea(University of New Mexico), David Osumi-Sutherland(European Bioinformatics Institute), Helen Parkinson(European Bioinformatics Institute), Zoë May Pendlington(European Bioinformatics Institute), Ana Rath(Inserm), Heidi L. Rehm(Broad Institute), Lyubov Remennik(National Cancer Institute), Erin Rooney Riggs, Paola Roncaglia(European Bioinformatics Institute), Justyne Ross(University of North Carolina at Chapel Hill), Marion Shadbolt(Biocom), Kent Shefchek(Helix (United States)), Morgan Similuk(National Institute of Allergy and Infectious Diseases), Nicholas Sioutos(National Cancer Institute), Damian Smedley(Queen Mary University of London), Rachel Sparks(National Institute of Allergy and Infectious Diseases), Ray Stefancsik(European Bioinformatics Institute), Ralf Stephan(National Institutes of Health), Andrea L. Storm(National Institutes of Health), Doron Stupp(Hebrew University of Jerusalem), Gregory S. Stupp(Scripps Research Institute), Jagadish Chandrabose Sundaramurthi(Jackson Laboratory), Imke Tammen(The University of Sydney), D. K. C. Tay(University of North Carolina at Chapel Hill), Courtney Thaxton(University of North Carolina at Chapel Hill), Eloise Valasek(Jewish General Hospital), Jordi Valls-Margarit(Bioinformatics Solutions (Canada)), Alex H. Wagner(Nationwide Children's Hospital), Danielle Welter(University of Luxembourg), Patricia L. Whetzel(European Bioinformatics Institute), Lori Whiteman(National Cancer Institute), Valerie Wood(University of Cambridge), Colleen Xu(Scripps Research Institute), Andreas Zankl(The University of Sydney), Xingmin Zhang(Jackson Laboratory), Christopher G. Chute(Johns Hopkins University), Peter N. Robinson(Jackson Laboratory), Chris Mungall(Lawrence Berkeley National Laboratory), Ada Hamosh(Johns Hopkins University), Melissa Haendel(University of Colorado Anschutz Medical Campus)
Cited by 100Open Access
Abstract
Abstract There are thousands of distinct disease entities and concepts, each of which are known by different and sometimes contradictory names. The lack of a unified system for managing these entities poses a major challenge for both machines and humans that need to harmonize information to better predict causes and treatments for disease. The Mondo Disease Ontology is an open, community-driven ontology that integrates key medical and biomedical terminologies, supporting disease data integration to improve diagnosis, treatment, and translational research. Mondo records the sources of all data and is continually updated, making it suitable for research and clinical applications that require up-to-date disease knowledge.
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