ArticleNucleic acids research2019
NetGO: improving large-scale protein function prediction with massive network information.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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Who cites it
68 citing papers in PubMed.
- DeepKOALA: a scalable deep learning framework for KEGG Orthology assignment.Briefings in bioinformatics · 2026Article
- On the state of protein function prediction: a report on the fourth CAFA challenge.bioRxiv : the preprint server for biology · 2026Article
- SSAS-GO: structure-sequence adaptive synergy network for protein function prediction.Briefings in bioinformatics · 2026Article
- Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- REPEL - Random Embedding Perturbation for Enhanced Learning of Protein Function.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- A scalable equivariant graph network framework for precise protein function prediction.Genome biology · 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
- GTPLM-GO: Enhancing Protein Function Prediction Through Dual-Branch Graph Transformer and Protein Language Model Fusing Sequence and Local-Global PPI Information.International journal of molecular sciences · 2025Article
- SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction.BMC bioinformatics · 2025Article
- NetGO 3.0: A Recent Protein Function Prediction Tool Based on Protein Language Model.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Graph Neural Network-Based Approaches for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Deep learning methods for protein function prediction.Proteomics · 2025Review
- 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
- A Survey of Biological Function Prediction Methods with Focus on Natural Language Processing (NLP) and Large Language Models (LLM).Methods in molecular biology (Clifton, N.J.) · 2025Article
- An Online Server for Geometry-Aware Protein Function Annotations Through Predicted Structure.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
- Annotating protein functions via fusing multiple biological modalities.Communications biology · 2024Article
- TAWFN: a deep learning framework for protein function prediction.Bioinformatics (Oxford, England) · 2024Article
8 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
Automated function prediction (AFP) of proteins is of great significance in biology. AFP can be regarded as a problem of the large-scale multi-label classification where a protein can be associated with multiple gene ontology terms as its labels. Based on our GOLabeler-a state-of-the-art method for the third critical assessment of functional annotation (CAFA3), in this paper we propose NetGO, a web server that is able to further improve the performance of the large-scale AFP by incorporating massive protein-protein network information. Specifically, the advantages of NetGO are threefold in using network information: (i) NetGO relies on a powerful learning to rank framework from machine learning to effectively integrate both sequence and network information of proteins; (ii) NetGO uses the massive network information of all species (>2000) in STRING (other than only some specific species) and (iii) NetGO still can use network information to annotate a protein by homology transfer, even if it is not contained in STRING. Separating training and testing data with the same time-delayed settings of CAFA, we comprehensively examined the performance of NetGO. Experimental results have clearly demonstrated that NetGO significantly outperforms GOLabeler and other competing methods. The NetGO web server is freely available at http://issubmission.sjtu.edu.cn/netgo/.
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