ArticleProtein science : a publication of the Protein Society2025
GOBeacon: An ensemble model for protein function prediction enhanced by contrastive learning.
Article in Protein science : a publication of the Protein Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- On the state of protein function prediction: a report on the fourth CAFA challenge.bioRxiv : the preprint server for biology · 2026Article
- A unified multimodal model for generalizable zero-shot and supervised protein function prediction.Bioinformatics (Oxford, England) · 2026Article
- eSIG-Net: Accurate prediction of single-mutation induced perturbations on protein interactions using a language model.bioRxiv : the preprint server for biology · 2026Article
- Structure-guided sequence representation learning for generalizable protein function prediction.Bioinformatics (Oxford, England) · 2025Article
- GOBeacon: An ensemble model for protein function prediction enhanced by contrastive learning.Protein science : a publication of the Protein Society · 2025Article
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Authors and funding
4 authors.
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Abstract
Accurate prediction of protein function is fundamental to understanding biological processes, with computational methods becoming increasingly essential as experimental methods struggle to keep pace with the rate of newly discovered proteins. Despite significant advances in machine learning approaches, existing methods often fail to capture the complex relationships between protein structure, evolution, and function, leading to limited prediction accuracy. The challenge lies in effectively integrating diverse biological data types while maintaining computational efficiency. Here, we show that GOBeacon, a novel ensemble model integrating structure-aware protein language model embeddings with protein-protein interaction networks, achieves high accuracy in protein function prediction. By employing a contrastive learning framework, GOBeacon demonstrates superior performance on the sequence-based CAFA3 benchmark, achieving F
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