ArticleGigaScience2020
Graph2GO: a multi-modal attributed network embedding method for inferring protein functions.
Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- IMAGO: An Improved Model Based on Attention Mechanism for Enhanced Protein Function Prediction.Biomolecules · 2025Article
- POSA-GO: Fusion of Hierarchical Gene Ontology and Protein Language Models for Protein Function Prediction.International journal of molecular sciences · 2025Article
- Deep learning methods for protein function prediction.Proteomics · 2025Review
- Integrating Gene Ontology Relationships for Protein Function Prediction Using PFresGO.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Annotating protein functions via fusing multiple biological modalities.Communications biology · 2024Article
- DeepPFP: a multi-task-aware architecture for protein function prediction.Briefings in bioinformatics · 2024Article
- DualNetGO: a dual network model for protein function prediction via effective feature selection.Bioinformatics (Oxford, England) · 2024Article
- A comprehensive review and comparison of existing computational methods for protein function prediction.Briefings in bioinformatics · 2024Review
- DeepSS2GO: protein function prediction from secondary structure.Briefings in bioinformatics · 2024Article
- Review
- SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data.Bioinformatics (Oxford, England) · 2023Article
- CFAGO: cross-fusion of network and attributes based on attention mechanism for protein function prediction.Bioinformatics (Oxford, England) · 2023Article
- Graph representation learning in biomedicine and healthcare.Nature biomedical engineering · 2022Review
- Article
- Predicting genetic interactions, cell line dependencies and drug sensitivities with variational graph auto-encoder.Frontiers in bioinformatics · 2022Article
- PANNZER-A practical tool for protein function prediction.Protein science : a publication of the Protein Society · 2022Article
- Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects.Briefings in bioinformatics · 2021Review
- TALE: Transformer-based protein function Annotation with joint sequence-Label Embedding.Bioinformatics (Oxford, England) · 2021Article
- DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction.Bioinformatics (Oxford, England) · 2021Article
- Machine learning predicts nucleosome binding modes of transcription factors.BMC bioinformatics · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying protein functions is important for many biological applications. Since experimental functional characterization of proteins is time-consuming and costly, accurate and efficient computational methods for predicting protein functions are in great demand for generating the testable hypotheses guiding large-scale experiments."
resultsHere, we propose Graph2GO, a multi-modal graph-based representation learning model that can integrate heterogeneous information, including multiple types of interaction networks (sequence similarity network and protein-protein interaction network) and protein features (amino acid sequence, subcellular location, and protein domains) to predict protein functions on gene ontology. Comparing Graph2GO to BLAST, as a baseline model, and to two popular protein function prediction methods (Mashup and deepNF), we demonstrated that our model can achieve state-of-the-art performance. We show the robustness of our model by testing on multiple species. We also provide a web server supporting function query and downstream analysis on-the-fly.
conclusionsGraph2GO is the first model that has utilized attributed network representation learning methods to model both interaction networks and protein features for predicting protein functions, and achieved promising performance. Our model can be easily extended to include more protein features to further improve the performance. Besides, Graph2GO is also applicable to other application scenarios involving biological networks, and the learned latent representations can be used as feature inputs for machine learning tasks in various downstream analyses.
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