ArticleBioinformatics (Oxford, England)2023
PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 43 citations in OpenAlex.
- A Novel Unsupervised Learning Model with Bayesian Hyperparameter Optimization for Predicting Substrates of Promiscuous Enzymes.ACS omega · 2026Article
- STAR-GO: improving protein function prediction by learning to hierarchically integrate ontology-informed semantic embeddings.Bioinformatics (Oxford, England) · 2026Article
- S2DA-GO: enhancing protein function prediction via gradient-decoupled cross-attention and semantic priors.Frontiers in genetics · 2026Article
- An efficient ranking deep neural network algorithm for the prediction of Ca2+ binding sites of the protein.PloS one · 2026Article
- MAGIN-GO: Protein function prediction based on dual graph neural networks and gene ontology structure.PloS one · 2026Article
- A scalable equivariant graph network framework for precise protein function prediction.Genome biology · 2025Article
- Adaptive gradient scaling: integrating Adam and landscape modification for protein structure prediction.BMC bioinformatics · 2025Article
- Article
- SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction.BMC bioinformatics · 2025Article
- SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations.Genomics, proteomics & bioinformatics · 2025Article
- Integrating Gene Ontology Relationships for Protein Function Prediction Using PFresGO.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Optimizing gene selection and module identification via ontology-based scoring and deep learning.Bioinformatics advances · 2025Article
- Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review.Frontiers in bioengineering and biotechnology · 2025Review
- Deep learning-based investigation of chloroplast translation regulatory sequences.Frontiers in plant science · 2025Article
- FuncFetch: an LLM-assisted workflow enables mining thousands of enzyme-substrate interactions from published manuscripts.Bioinformatics (Oxford, England) · 2024Article
- GOPhage: protein function annotation for bacteriophages by integrating the genomic context.Briefings in bioinformatics · 2024Article
- MORE: a multi-omics data-driven hypergraph integration network for biomedical data classification and biomarker identification.Briefings in bioinformatics · 2024Article
- Exploring protein natural diversity in environmental microbiomes with DeepMetagenome.Cell reports methods · 2024Article
- Advancing microRNA target site prediction with transformer and base-pairing patterns.Nucleic acids research · 2024Article
- GGN-GO: geometric graph networks for predicting protein function by multi-scale structure features.Briefings in bioinformatics · 2024Article
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
motivationThe rapid accumulation of high-throughput sequence data demands the development of effective and efficient data-driven computational methods to functionally annotate proteins. However, most current approaches used for functional annotation simply focus on the use of protein-level information but ignore inter-relationships among annotations.
resultsHere, we established PFresGO, an attention-based deep-learning approach that incorporates hierarchical structures in Gene Ontology (GO) graphs and advances in natural language processing algorithms for the functional annotation of proteins. PFresGO employs a self-attention operation to capture the inter-relationships of GO terms, updates its embedding accordingly and uses a cross-attention operation to project protein representations and GO embedding into a common latent space to identify global protein sequence patterns and local functional residues. We demonstrate that PFresGO consistently achieves superior performance across GO categories when compared with 'state-of-the-art' methods. Importantly, we show that PFresGO can identify functionally important residues in protein sequences by assessing the distribution of attention weightings. PFresGO should serve as an effective tool for the accurate functional annotation of proteins and functional domains within proteins. AVAILABILITY AND IMPLEMENTATION: PFresGO is available for academic purposes at https://github.com/BioColLab/PFresGO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
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.