Authors
Rashika Ramola, M Clara De Paolis Kaluza, Damiano Piovesan, Yisu Peng, Parnal Joshi, Mahta Mehdiabadi, Federica Quaglia, Rita Pancsa, Lucía B Chemes, Hongryul Ahn, Adrian M Altenhoff, Ehsaneddin Asgari, Maria Cristina Aspromonte, Volkan Atalay, Giulia Babbi, Davide Baldazzi, Meet M Barot, Asa Ben-Hur, Alfredo Benso, Daniel Berenberg, Jari Björne, Florian Boecker, Paolo Boldi, Joseph Bonello, Nicola Bordin, Piyush Borole, Ali Ebrahimpour Boroojeny, Renzhi Cao, Stefano Di Carlo, Rita Casadio, Elena Casiraghi, Jia-Ming Chang, Chen Chen, Tse-Ming Chen, Jianlin Cheng, Ssu Chiu, Alperen Dalkıran, Radoslav S Davidović, Christophe Dessimoz, Rucheng Diao, Warith Eddine Djeddi, Tunca Dogan, Sean T Flannery, Paolo Fontana, Marco Frasca, Lydia Freddolino, Branislava Gemović, Jesse Gillis, Filip Ginter, Vladimir Gligorijevic, Giuliano Grossi, Michael Heinzinger, Kyle Hippe, Robert Hoehndorf, Liisa Holm, Jie Hou, John R Hover, Yen-Ting Huang, Emilio Ispano, Suraiya Jabin, Aashish Jain, David T Jones, Suwisa Kaewphan, Yuki Kagaya, Jenna Kanerva, Daisuke Kihara, Maxat Kulmanov, Sunil Kumar, Lukasz Kurgan, Enrico Lavezzo, Jon Lees, Wen-Hung Liao, Han Lin, Michal Linial, Maria Littmann, Lizhi Liu, Tong Liu, Yi Wei Liu, Stavros Makrodimitris, Laura Manuto, Pier Luigi Martelli, Alice Carolyn Mchardy, Gabriela A Merino, Diego H Milone, Sarthak Mishra, Mohammad R K Mofrad, David Moi, Tsukasa Nakamura, Vijay Kumar Narsapuram, Maria Victoria Nugnes, Takeshi Obayashi, Dan Ofer, Alberto Paccanaro, Vladimir R Perovic, Alessandro Petrini, Gianfranco Politano, Daniele Raimondi, Nadav Rappoport, Hafeez Ur Rehman, Maarten J M F Reijnders, Marcel J T Reinders, P Douglas Renfrew, Ahmet S Rifaioglu, Alfonso E Romero, Abhiman Saraswathi, Castrense Savojardo, Harry M Scholes, Heiko Schoof, Yang Shen, Ian Sillitoe, Georgina Stegmayer, Amos Stern, Henri Tiittanen, Sumyyah Toonsi, Stefano Toppo, Petri Toronen, Mateo Torres, Gabriella Trucco, Giorgio Valentini, Nevena Veljkovic, Alex Warwick Vesztrocy, Vedrana Vidulin, Amelia Villegas-Morcillo, Antti Virtanen, Wim Vranken, Slobodan Vucetic, Cen Wan, Zheng Wang, Mark N Wass, Robert M Waterhouse, Sadok Ben Yahia, Haixuan Yang, Shuwei Yao, Ronghui You, Jeffrey Yunes, Chengxin Zhang, Yang Zhang, Chenguang Zhao, Xiaogen Zhou, Yi-Heng Zhu, Shanfeng Zhu, Hao Zhu, Gökhan Özsari, Burkhard Rost, Christine Orengo, Marc Robinson-Rechavi, Dannie Durand, Steven E Brenner, Casey S Greene, Sean D Mooney, Silvio C E Tosatto, Iddo Friedberg, Predrag Radivojac
Published in
bioRxiv : the preprint server for biology. Jun 12, 2026. Epub Jun 12, 2026.
Abstract
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).
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.
PMID:
42146430
Bibliographic data and abstract were imported from PubMed on 13 Oct 2026.
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