ArticleNucleic acids research2021
NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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59 citing papers in PubMed.
- Protein function-conditioned language models for variant effect prediction and controllable design.Biophysical journal · 2026Article
- Predicting functions of uncharacterized gene products from microbial communities.Nature biotechnology · 2026Article
- On the state of protein function prediction: a report on the fourth CAFA challenge.bioRxiv : the preprint server for biology · 2026Article
- Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Evaluation of the surface lipoprotein assembly modifier (Slam) as a vaccine candidate against Neisseria gonorrhoeae infection.Scientific reports · 2026Article
- Computational approaches to enzymatic reaction assignment: a review of methods, validations, and future directions.Briefings in bioinformatics · 2025Review
- Proteomic Analysis of Mechanical Injury Effects in Papaya Fruit at Two Maturity Stages.Proteomes · 2025Article
- PF-AGCN: an adaptive graph convolutional network for protein-protein interaction-based function prediction.Bioinformatics (Oxford, England) · 2025Article
- SPACE: STRING proteins as complementary embeddings.Bioinformatics (Oxford, England) · 2025Article
- EZpred: improving deep learning-based enzyme function prediction using unlabeled sequence homologs.bioRxiv : the preprint server for biology · 2025Article
- Discovery of diverse and high-quality mRNA capping enzymes through a language model-enabled platform.Science advances · 2025Article
- Functional profiling of the sequence stockpile: a protein pair-based assessment of in silico prediction tools.Bioinformatics (Oxford, England) · 2025Article
- Predicting Protein Functions with Function-Aware Domain Embeddings Using Domain-PFP.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review.Frontiers in bioengineering and biotechnology · 2025Review
- An Online Server for Geometry-Aware Protein Function Annotations Through Predicted Structure.Methods in molecular biology (Clifton, N.J.) · 2025Article
- ProtFun: a protein function prediction model using graph attention networks with a protein large language model.Bioinformatics advances · 2025Article
- Deep learning methods for protein function prediction.Proteomics · 2025Review
- NetGO 3.0: A Recent Protein Function Prediction Tool Based on Protein Language Model.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Machine Learning for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- PLMSearch and PLMAlign: Protein Language Model (PLM)-Based Homologous Protein Sequence Search and Alignment.Methods in molecular biology (Clifton, N.J.) · 2025Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
With the explosive growth of protein sequences, large-scale automated protein function prediction (AFP) is becoming challenging. A protein is usually associated with dozens of gene ontology (GO) terms. Therefore, AFP is regarded as a problem of large-scale multi-label classification. Under the learning to rank (LTR) framework, our previous NetGO tool integrated massive networks and multi-type information about protein sequences to achieve good performance by dealing with all possible GO terms (>44 000). In this work, we propose the updated version as NetGO 2.0, which further improves the performance of large-scale AFP. NetGO 2.0 also incorporates literature information by logistic regression and deep sequence information by recurrent neural network (RNN) into the framework. We generate datasets following the critical assessment of functional annotation (CAFA) protocol. Experiment results show that NetGO 2.0 outperformed NetGO significantly in biological process ontology (BPO) and cellular component ontology (CCO). In particular, NetGO 2.0 achieved a 12.6% improvement over NetGO in terms of area under precision-recall curve (AUPR) in BPO and around 2.6% in terms of $\mathbf {F_{max}}$ in CCO. These results demonstrate the benefits of incorporating text and deep sequence information for the functional annotation of BPO and CCO. The NetGO 2.0 web server is freely available at http://issubmission.sjtu.edu.cn/ng2/.
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