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Accès ouvert déclaré 2026 preprint

On the state of protein function prediction: a report on the fourth CAFA challenge

0Citations signalées — pas une note de qualité
96Institutions déclarées
33Pays d’affiliation déclarés

Résumé fourni par la source

Background: The Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). Results: CAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced "partial knowledge" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.

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Contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
On the state of protein function prediction: a report on the fourth CAFA challenge
Date Crossref
11/05/2026
Éditeur
openRxiv
Type
posted-content

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.

Institutions déclarées

Northeastern UniversityUniversity of PaduaIowa State UniversityHUN-REN Research Centre for Natural SciencesNational University of General San MartínUniversity of SuwonSIB Swiss Institute of BioinformaticsETH ZurichQatar Airways (Qatar)Middle East Technical UniversityGNA UniversityUniversity of BolognaNational Cancer InstituteNew York UniversityColorado State UniversityPolitecnico di TorinoUniversity of TurkuUniversity of BonnUniversity of MilanUniversity of MaltaInstitute of Structural and Molecular BiologyUniversity College LondonTemple UniversityUniversity of Illinois Urbana-ChampaignPacific Lutheran UniversityLawrence Berkeley National LaboratoryNational Chengchi UniversityUniversity of MissouriUniversity of Missouri Health SystemUniversity of BelgradeInstitute of Physics BelgradeUniversity of LausanneUniversity of MichiganUniversity of JendoubaTunis El Manar UniversityHacettepe UniversityPurdue University West LafayetteFondazione Edmund MachUniversity of TorontoCold Spring Harbor LaboratoryTechnical University of MunichUniversity of ChicagoKing Abdullah University of Science and TechnologyUniversity of HelsinkiSaint Louis UniversityJamia Millia IslamiaCorteva (United States)Virginia Commonwealth UniversityUniversity of BristolNational Taiwan UniversityNational Taipei UniversityHebrew University of JerusalemFudan UniversityUniversity of MiamiErasmus MC Cancer InstituteDelft University of TechnologyBiology of InfectionEuropean Bioinformatics InstituteConsejo Nacional de Investigaciones Científicas y TécnicasUniversity of California, BerkeleyTohoku UniversityRoyal Holloway University of LondonFundação Getulio VargasHuawei Technologies (France)KU LeuvenBen-Gurion University of the NegevNational University of Computer and Emerging SciencesLiverpool John Moores UniversityFlatiron Health (United States)Flatiron InstituteTexas A&M UniversityUniversidad Nacional del LitoralEP Analytics (United States)Universidad de GranadaBirkbeck, University of LondonSylvester Comprehensive Cancer CenterUniversity of KentTallinn University of TechnologyUniversity of Southern DenmarkOllscoil na Gaillimhe – University of GalwayShanghai Center for Brain Science and Brain-Inspired TechnologyNankai UniversityInstitute on AgingChinese Academy of SciencesShenzhen Institutes of Advanced TechnologyNational University of SingaporeNational University Cancer Institute, SingaporeSaint Ambrose UniversityZhejiang University of TechnologyNanjing Agricultural UniversityFlorida Memorial UniversityChalmers University of TechnologyCarnegie Mellon UniversityUniversity of Colorado AnschutzNational Institutes of HealthCenter for Information Technology

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Sujets associés

Bioinformatics and Genomic NetworksBiomedical Text Mining and OntologiesMachine Learning in Bioinformatics

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