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The Human Phenotype Ontology in 2024: phenotypes around the world
Michael Gargano, Nicolas Matentzoglu, Ben Coleman, Eunice B Addo-Lartey, Anna V. Anagnostopoulos, Joel Anderton, Paul Avillach, Anita Bagley, Eduard Bakštein, James P. Balhoff, Gareth Baynam, Susan M. Bello, Michael Berk, Holli Bertram, Somer Bishop, Hannah Blau, David F. Bodenstein, Pablo Botas, Kaan Boztuǧ, J Cady, Tiffany J. Callahan, Rhiannon Cameron, Seth Carbon, F Castellanos, J. Harry Caufield, Lauren Chan, Christopher G. Chute, Noémi Dahan‐Oliel, Jon R. Davids, Maud de Dieuleveult, Vinícius de Souza, Bert B.A. de Vries, Esther de Vries, J. Raymond DePaulo, Beáta Dérfalvi, Ferdinand Dhombres, Claudia Diaz‐Byrd, Alexander J.M. Dingemans, Bruno Donadille, Michael Duyzend, Reem Elfeky, Shahim Essaid, Carolina Fabrizzi, Giovanna Fico, Helen V. Firth, Yun Freudenberg‐Hua, Janice M. Fullerton, Davera Gabriel, Kimberly Gilmour, Jessica L. Giordano, Fernando S. Goes, Rachel Gore Moses, Ian Green, Matthias Griese, Tudor Groza, Weihong Gu, Julia Guthrie, Benjamin M. Gyori, Ada Hamosh, Marc Hanauer, Kateřina Hanušová, Yongqun He, Harshad Hegde, Ingo Helbig, Kateřina Holasová, Charles Tapley Hoyt, Shangzhi Huang, Eric Hurwitz, Julius O.B. Jacobsen, Xiaofeng Jiang, Lisa Joseph, Kamyar Keramatian, Bryan King, Katrin Knoflach, David A. Koolen, Megan L Kraus, Carlo Kroll, Maaike Kusters, Markus S. Ladewig, David Lagorce, Meng‐Chuan Lai, Pablo Lapunzina, Bryan Laraway, David Lewis‐Smith, Xiarong Li, Caterina Lucano, Marzieh Majd, Mary L. Marazita, Víctor Martínez‐Glez, Toby H McHenry, Melvin G. McInnis, Julie A. McMurry, Michaela Mihulová, Caitlin E. Millett, Philip B. Mitchell, Veronika Moslerová, Kenji Narutomi, Shahrzad Nematollahi, Julián Nevado, Andrew A. Nierenberg, Nikola Novák Čajbiková, John I. Nürnberger, Soichi Ogishima, Daniel Olson, Abigail Ortiz, Harry Pachajoa, Guiomar Pérez de Nanclares, Amy T. Peters, Tim Putman, Christina Rapp, Ana Rath, Justin Reese, Lauren Rekerle, Angharad M. Roberts, Suzy Roy, Stephan Sanders, Catharina Schuetz, Eva C. Schulte, Thomas G. Schulze, Martin Schwarz, Katie Scott, Dominik Seelow, Berthold Seitz, Yiping Shen, Morgan Similuk, Eric S. Simon, Balwinder Singh, Damian Smedley, Cynthia L. Smith, Jake T Smolinsky, Sarah H. Sperry, Elizabeth Stafford, Ray Stefancsik, Robin Steinhaus, Rebecca Strawbridge, Jagadish Chandrabose Sundaramurthi, Polina Talapova, Jair Tenorio, Pavel Tesner, Rhys H. Thomas, Audrey Thurm, Marek Turnovec, Mariëlle van Gijn, Nicole Vasilevsky, Markéta Vlčková, Anita Walden, Kai Wang, Ronald J. Wapner, James S. Ware, Addo A Wiafe, Samuel Agyei Wiafe, Lisa D. Wiggins, Andrew E. Williams, Chen Wu, Margot J. Wyrwoll, Hui Xiong, Nefize Yalın, Yasunori Yamamoto, Lakshmi N. Yatham, Anastasia K. Yocum, Allan H. Young, Zafer Yüksel, Peter P. Zandi, Andreas Zankl, Ignacio Zarante, Miroslav Zvolský, Sabrina Toro, Leigh Carmody, Nomi L. Harris, Mónica Muñoz-Torres, Daniel Daniš, Chris Mungall, Sebastian Köhler, Melissa Haendel, Peter N. Robinson
Bibliographic record
VenueNucleic Acids Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsMcGill UniversityCentre for Addiction and Mental HealthUniversity of British ColumbiaUniversity of TorontoDalhousie UniversityShriners Hospitals for Children - CanadaSimon Fraser University
FundersNational Institute of Dental and Craniofacial ResearchNational Institute of Child Health and Human DevelopmentNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthU.S. National Library of MedicineDefense Advanced Research Projects AgencyMcCusker Charitable FoundationNational Institutes of HealthDeutsche ForschungsgemeinschaftAngela Wright Bennett FoundationBerlin Institute of HealthNational Center for Advancing Translational SciencesNational Human Genome Research InstituteWellcome TrustNational Cancer InstituteBritish Heart FoundationNational Institute on AgingHorizon 2020 Framework ProgrammeEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentU.S. Department of Energy
KeywordsOntologyClinical phenotypeBiologyInferencePhenotypeData scienceDiseaseAnalyticsComputer scienceComputational biologyBioinformaticsArtificial intelligenceGeneGeneticsMedicinePathology
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.055
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.042
GPT teacher head0.357
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations375
Published2023
Admission routes1
Has abstractyes