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Record W4285084440 · doi:10.1038/s41587-022-01369-0

Standardized annotation of translated open reading frames

2022· letter· en· W4285084440 on OpenAlexafffund
Jonathan M. Mudge, Jorge Ruiz‐Orera, John R. Prensner, Marie A. Brunet, Ferriol Calvet, Irwin Jungreis, José M. González, Michele Magrane, Thomas F. Martínez, Jana Felicitas Schulz, Yucheng Yang, M. Mar Albà, Julie L. Aspden, Pavel V. Baranov, Ariel Bazzini, Elspeth A. Bruford, María Martin, Lorenzo Calviello, Anne‐Ruxandra Carvunis, Jin Chen, Juan Pablo Couso, Eric W. Deutsch, Paul Flicek, Adam Frankish, Mark Gerstein, Norbert Hübner, Nicholas T. Ingolia, Manolis Kellis, Gerben Menschaert, Robert L. Moritz, Uwe Ohler, Xavier Roucou, Alan Saghatelian, Jonathan S. Weissman, Sebastiaan van Heesch

Bibliographic record

VenueNature Biotechnology · 2022
Typeletter
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversité de Sherbrooke
FundersNational Cancer InstituteNational Human Genome Research InstituteNational Institute on AgingRIKENAgència de Gestió d'Ajuts Universitaris i de RecercaMedical Research CouncilCanadian Institutes of Health ResearchDirectorate for Biological SciencesNational Institutes of HealthEuropean Molecular Biology LaboratoryRussian Science FoundationInstitució Catalana de Recerca i Estudis AvançatsNational Institute of General Medical SciencesUniversity College CorkUniversity of LeedsUniversitat Pompeu FabraFondation LeducqStowers Institute for Medical ResearchAustralian GovernmentSearle Scholars ProgramNational Science FoundationScience Foundation IrelandBiotechnology and Biological Sciences Research CouncilUniversité de SherbrookeWellcome TrustEuropean CommissionUniversity of California, IrvineBroad InstituteUniversity of PittsburghHoward Hughes Medical InstituteStaatssekretariat für Bildung, Forschung und InnovationMusella Foundation For Brain Tumor Research and InformationAgencia Estatal de InvestigaciónComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyAlex's Lemonade Stand Foundation for Childhood CancerYale University
KeywordsAnnotationOpen reading frameReading (process)Computer scienceInformation retrievalComputational biologyNatural language processingBiologyArtificial intelligenceLinguisticsGeneticsPhilosophyPeptide sequence

Abstract

fetched live from OpenAlex

Open Access articles citing this article. The importance of being the HGNC Elspeth A. Bruford , Bryony Braschi … Susan Tweedie Human Genomics Open Access 15 November 2022 OpenVar: functional annotation of variants in non-canonical open reading frames Marie A. Brunet , Sébastien Leblanc & Xavier Roucou Cell & Bioscience Open Access 14 August 2022

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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)0.007
metaresearch head score (Gemma)0.014
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: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.009

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 head0.010
GPT teacher head0.294
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine 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

Citations250
Published2022
Admission routes2
Has abstractyes

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