Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources
Open accessAbstract
The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases-is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO's interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.
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These genes demonstrated significant differences between AD and control samples.
“These genes demonstrated significant differences between AD and control samples.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [68]
The Disease Annotations section describes HPO annotations and gene–disease associations, not a comparison of AD and control samples. Searches for the claimed significant difference, Alzheimer’s disease, and differentially expressed genes found no passage reporting this result; the returned References passages do not establish it either. Page numbers were unavailable in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measuredThe model's validity was assessed using the top 20 and bottom 10 genes across three biologically distinct AD model systems.
“The model's validity was assessed using the top 20 and bottom 10 genes across three biologically distinct AD model systems (68).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [68]
The Introduction (page unavailable) describes HPO resources and their use in phenotype-driven genomic diagnostics, not a validation study across AD model systems. Searches for the top 20 and bottom 10 genes and for Alzheimer’s disease model-system validation found no passage addressing the claimed assessment. (LLM, conf=0.77)
Experimental — accuracy not yet measuredAlthough constrained by input data that lacked non-human information, the AD-specific XGBoost model emphasized the role of infection as a critical factor in AD pathology.
“Although constrained by input data that lacked non-human information, the AD-specific XGBoost model emphasized the role of infection as a critical factor in AD pathology.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [68]
The Introduction discusses HPO phenotypes and their use with non-human model-organism data, but does not describe an AD-specific XGBoost model. Searches for XGBoost, non-human input limitations, and infection in AD pathology found no passage addressing the claim. (LLM, conf=0.75)
Experimental — accuracy not yet measuredXGBoost was utilized to identify novel genes associated with Alzheimer's disease.
“In this case, XGBoost, a form of extreme gradient boosting (67), was utilized to identify novel genes associated with Alzheimer's disease (68).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [68]
The Introduction describes HPO resources for deep phenotyping in rare diseases, and the EXOMISER UPDATE describes phenotype-based variant prioritization. Searches for XGBoost, extreme gradient boosting, and Alzheimer’s disease found no passage saying that XGBoost identified novel Alzheimer’s-associated genes. (LLM, conf=0.77)
Experimental — accuracy not yet measuredThis approach uncovered five previously unassociated gene targets related to immunity in AD: FRRS1, CTRAM, SCGB3A1, CIBAR2, and TMEFF2.
“This approach uncovered five previously unassociated gene targets related to immunity in AD: FRRS1 , CTRAM , SCGB3A1 , CIBAR2 , and TMEFF2.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [68]
The retrieved passages from the paper’s References and Introduction (page numbers unavailable) concern HPO resources and rare-disease gene discovery, not five immunity-related gene targets in AD. Searches for the five named genes, including TMEFF2, and for immunity-related targets in AD found no passage addressing the claim. (LLM, conf=0.74)
Experimental — accuracy not yet measured
The model identified key variables associated with inflammatory processes and oxidative stress.
“The model identified key variables associated with inflammatory processes and oxidative stress.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [68]
The Disease Annotations section describes new HPO phenotype terms and disease annotations, not a model identifying variables linked to inflammatory processes or oxidative stress. Searches for that phrase and for a model identifying inflammation- or oxidative-stress-related variables found no supporting passage in our copy; page numbers were not provided. (LLM, conf=0.76)
Experimental — accuracy not yet measuredAn AD-focused model was developed, which included 53 positive (AD-associated) and 3,952 negative (non-AD-associated) genes.
“By integrating the comprehensive Pharos dataset, a web-based interface for data collected by the Illuminating the Druggable Genome initiative, into XGBoost, an AD-focused model was developed, which included 53 positive (AD-associated) and 3,952 negative (non-AD-associated) genes.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [68]
The Introduction describes HPO-based rare-disease gene prioritization, and Disease Annotations describes additions to the ontology; neither reports an AD-focused model. Searches for Pharos, XGBoost, and the 53-positive/3,952-negative gene counts found no passage addressing the claim in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Mouse Genome Informatics (MGI): latest news from MGD and GXD
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Encoding Clinical Data with the Human Phenotype Ontology for Computational Differential Diagnostics
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- Authors
- Köhler, S., Carmody, L., Vasilevsky, N., Jacobsen, J. O. B., Danis, D., Gourdine, J.-P., Gargano, M., Harris, N. L., Matentzoglu, N., McMurry, J. A., Osumi-Sutherland, D., Cipriani, V., Balhoff, J. P., Conlin, T., Blau, H., Baynam, G., Palmer, R., Gratian, D., Dawkins, H., Segal, M., Jansen, A. C., Muaz, A., Chang, W. H., Bergerson, J., Laulederkind, S. J. F., Yüksel, Z., Beltran, S., Freeman, A. F., Sergouniotis, P. I., Durkin, D., Storm, A. L., Hanauer, M., Brudno, M., Bello, S. M., Sincan, M., Rageth, K., Wheeler, M. T., Oegema, R., Lourghi, H., Della Rocca, M. G., Thompson, R., Castellanos, F., Priest, J., Cunningham-Rundles, C., Hegde, A., Lovering, R. C., Hajek, C., Olry, A., Notarangelo, L., Similuk, M., Zhang, X. A., Gómez-Andrés, D., Lochmüller, H., Dollfus, H., Rosenzweig, S., Marwaha, S., Rath, A., Sullivan, K., Smith, C., Milner, J. D., Leroux, D., Boerkoel, C. F., Klion, A., Carter, M. C., Groza, T., Smedley, D., Haendel, M. A., Mungall, C., & Robinson, P. N.
- Affiliations
- Humboldt-Universität zu Berlin (DE)
- The Jackson Laboratory for Genomic Medicine (US)
- Oregon Health & Science University (US)
- Queen Mary University of London (GB)
- Monarch Initiative, monarchinitiative.org
- Lawrence Berkeley National Laboratory (US)
- European Bioinformatics Institute (GB)
- Oregon State University (US)
- University of North Carolina at Chapel Hill (US)
- The Kids Research Institute Australia (AU)
- Curtin University (AU)
- Government of Western Australia (AU)
- SimulConsult (US)
- Vrije Universiteit Brussel (BE)
- Garvan Institute of Medical Research (AU)
- University of Toronto (CA)
- National Institutes of Health (US)
- Marquette University (US)
- Bioscientia GmbH, Ingelheim, Germany
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona 08028, Spain
- Manchester Royal Eye Hospital (GB)
- Inserm (FR)
- Jackson Laboratory (US)
- Sanford Health (US)
- Stanford Medicine (US)
- University Medical Center Utrecht (NL)
- ICF, Rockville, MD, USA
- Newcastle University (GB)
- Mount Sinai School of Medicine, New York, NY, USA
- University College London (GB)
- Child Neurology Unit. Hospital Universitari Vall d’Hebron, Vall d’Hebron Research Institute (VHIR), Barcelona, Spain
- University of Ottawa (CA)
- Hôpitaux Universitaires de Strasbourg (FR)
- Children's Hospital of Philadelphia (US)
- University of Connecticut (US)
- Corresponding author
- Robinson, Peter N
- ORCID
- Köhler, Sebastian
- Carmody, Leigh
- Vasilevsky, Nicole
- Jacobsen, Julius O B
- Gourdine, Jean-Philippe
- Gargano, Michael
- Harris, Nomi L
- Matentzoglu, Nicolas
- McMurry, Julie A
- Osumi-Sutherland, David
- Cipriani, Valentina
- Balhoff, James P
- Conlin, Tom
- Blau, Hannah
- Baynam, Gareth
- Palmer, Richard
- Segal, Michael
- Jansen, Anna C
- Bergerson, Jenna
- Laulederkind, Stanley J F
- Yüksel, Zafer
- Beltran, Sergi
- Freeman, Alexandra F
- Sergouniotis, Panagiotis I
- Storm, Andrea L
- Hanauer, Marc
- Brudno, Michael
- Bello, Susan M
- Sincan, Murat
- Rageth, Kayli
- Wheeler, Matthew T
- Oegema, Renske
- Thompson, Rachel
- Castellanos, Francisco
- Priest, James
- Lovering, Ruth C
- Hajek, Catherine
- Olry, Annie
- Notarangelo, Luigi
- Similuk, Morgan
- Zhang, Xingmin A
- Lochmüller, Hanns
- Dollfus, Hélène
- Rosenzweig, Sergio
- Marwaha, Shruti
- Rath, Ana
- Sullivan, Kathleen
- Smith, Cynthia
- Milner, Joshua D
- Boerkoel, Cornelius F
- Klion, Amy
- Carter, Melody C
- Groza, Tudor
- Smedley, Damian
- Haendel, Melissa A
- Mungall, Chris
- Robinson, Peter N
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-11-22
- Volume
- 47
- Issue
- D1
- Pages
- D1018-D1027
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC6324074
- OpenAlex
- W2901332105
- Cited by
- 677
- References
- 64
- Citations by year
- 2026: 27 · 2025: 49 · 2024: 57 · 2023: 68 · 2022: 108 · 2021: 157 · 2020: 134 · 2019: 72 · 2018: 2 · 2016: 1
- FWCI
- 33.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (publishedVersion, cc-by)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
- Murdoch University Research Portal (submittedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion, other-oa)
- HAL (submittedVersion, cc-by)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eSpace (Curtin University) (submittedVersion, cc-by)
- The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion, cc-by)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, cc-by)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- National Institutes of Health (OD #5R24OD011883, 1 OT3 OD02464-01 UNCCH)
- Forums for Integrative Phenomics (U13 CA221044-01)
- NCATS Data Translator (1OT3TR002019)
- NCATS National Center for Digital Health Informatics Innovation (U24 TR002306)
- British Heart Foundation (RG/13/5/30112)
- Division of Intramural Research NIAID (01GM1608)
- European Union ’s Horizon 2020 Research and Innovation Programme (779257)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- Human Phenotype Ontology, phenotypic abnormalities, rare diseases, deep phenotyping, disease definition, ontology interoperability, Exomiser, variant interpretation, ontology mapping, clinical features, human genes
- MeSH terms
- Congenital Abnormalities, Whole Genome Sequencing, Humans, Phenotype, Genetic Variation, Computational Biology, Genetic Predisposition to Disease, Internet, Databases, Genetic, Rare Diseases, Knowledge Bases, Biological Ontologies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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