ArticleNature methods2013
A large-scale evaluation of computational protein function prediction.
Article in Nature methods, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 515 papers.
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Who cites it
515 citing papers in PubMed.
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- AlphaGEM enables precise genome-scale metabolic modelling by integrating protein structure alignment with deep-learning-based dark metabolism mining.Nature communications · 2026Article
- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- Predicting functions of uncharacterized gene products from microbial communities.Nature biotechnology · 2026Article
- How not to be seen: predicting unseen enzyme functions using contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- Overestimating zero-shot fitness prediction: Broad benchmarks mask local failures and practical limitations.bioRxiv : the preprint server for biology · 2026Article
- On the state of protein function prediction: a report on the fourth CAFA challenge.bioRxiv : the preprint server for biology · 2026Article
- Advances in Protein Function Prediction from the Fifth CAFA Challenge.bioRxiv : the preprint server for biology · 2026Article
- LAFA: A Framework for Reproducible Longitudinal Assessment of Protein Function Annotation Models.ArXiv · 2026Article
- ProtDML: label-aware representation learning for broad-spectrum protein function prediction.Briefings in bioinformatics · 2026Article
- SSAS-GO: structure-sequence adaptive synergy network for protein function prediction.Briefings in bioinformatics · 2026Article
- Evaluating completeness, coherence, and consistency of genome-scale function annotations.Briefings in bioinformatics · 2026Review
- Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Evaluating network-based algorithms for protein function prediction using plant protein-protein interaction networks.BMC plant biology · 2026Article
- The disputed identity ofmBio · 2026Article
455 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
102 authors.
Funding
Abstract
Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.
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Registered trials
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