ArticleBioinformatics (Oxford, England)2021
DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed.
- Predicting membrane protein localization by deep learning on structure and chemistry.Protein science : a publication of the Protein Society · 2026Article
- GPCR-GO: Relation-aware graph learning for predicting Gene Ontology terms of G protein-coupled receptors.PLoS computational biology · 2026Article
- 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
- Evaluating completeness, coherence, and consistency of genome-scale function annotations.Briefings in bioinformatics · 2026Review
- Out-of-distribution generalization enhances protein function annotation for low-homology sequences.Briefings in bioinformatics · 2026Article
- Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- STAR-GO: improving protein function prediction by learning to hierarchically integrate ontology-informed semantic embeddings.Bioinformatics (Oxford, England) · 2026Article
- Graph Learning in Bioinformatics: A Survey of Graph Neural Network Architectures, Biological Graph Construction and Bioinformatics Applications.Biomolecules · 2026Review
- Struct2GO-Enhanced: Multimodal Graph Attention Improves Protein Function Prediction.Journal of chemical information and modeling · 2026Article
- MAGIN-GO: Protein function prediction based on dual graph neural networks and gene ontology structure.PloS one · 2026Article
- Topological and functional characterization of human translation efficiency covariation network.Bioinformatics (Oxford, England) · 2025Article
- Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review.Applied biochemistry and biotechnology · 2025Review
- GOAnnotator: accurate protein function annotation using automatically retrieved literature.Bioinformatics (Oxford, England) · 2025Article
- ProtGO: universal protein function prediction utilizing multi-modal gene ontology knowledge.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
- POSA-GO: Fusion of Hierarchical Gene Ontology and Protein Language Models for Protein Function Prediction.International journal of molecular sciences · 2025Article
- MSNGO: multi-species protein function annotation based on 3D protein structure and network propagation.Bioinformatics (Oxford, England) · 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
3 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
motivationAutomated function prediction (AFP) of proteins is a large-scale multi-label classification problem. Two limitations of most network-based methods for AFP are (i) a single model must be trained for each species and (ii) protein sequence information is totally ignored. These limitations cause weaker performance than sequence-based methods. Thus, the challenge is how to develop a powerful network-based method for AFP to overcome these limitations.
resultsWe propose DeepGraphGO, an end-to-end, multispecies graph neural network-based method for AFP, which makes the most of both protein sequence and high-order protein network information. Our multispecies strategy allows one single model to be trained for all species, indicating a larger number of training samples than existing methods. Extensive experiments with a large-scale dataset show that DeepGraphGO outperforms a number of competing state-of-the-art methods significantly, including DeepGOPlus and three representative network-based methods: GeneMANIA, deepNF and clusDCA. We further confirm the effectiveness of our multispecies strategy and the advantage of DeepGraphGO over so-called difficult proteins. Finally, we integrate DeepGraphGO into the state-of-the-art ensemble method, NetGO, as a component and achieve a further performance improvement. AVAILABILITY AND IMPLEMENTATION: https://github.com/yourh/DeepGraphGO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.