ArticlePeerJ2021
Protein function prediction with gene ontology: from traditional to deep learning models.
Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- MiMeDB 2.0: the Human Microbial Metabolome Database for 2026.Nucleic acids research · 2026Article
- Interpretable prediction of nucleic acid-binding proteins using a protein language model.Bioinformatics advances · 2026Article
- Whole-genome sequencing and probiotic properties of Lactobacillus helveticus KM7 isolated from the gut of the Chinese honey bee (Apis cerana): A promising exopolysaccharide-producing strain.BMC microbiology · 2025Article
- Graph Neural Network-Based Approaches for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Prediction and validation of nanowire proteins inComputational and structural biotechnology journal · 2025Article
- Article
- Review
- An informatic workflow for the enhanced annotation of excretory/secretory proteins ofComputational and structural biotechnology journal · 2023Article
- Computational prediction of disordered binding regions.Computational and structural biotechnology journal · 2023Review
- GOProFormer: A Multi-Modal Transformer Method for Gene Ontology Protein Function Prediction.Biomolecules · 2022Article
- Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom.Plants (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein function prediction is a crucial part of genome annotation. Prediction methods have recently witnessed rapid development, owing to the emergence of high-throughput sequencing technologies. Among the available databases for identifying protein function terms, Gene Ontology (GO) is an important resource that describes the functional properties of proteins. Researchers are employing various approaches to efficiently predict the GO terms. Meanwhile, deep learning, a fast-evolving discipline in data-driven approach, exhibits impressive potential with respect to assigning GO terms to amino acid sequences. Herein, we reviewed the currently available computational GO annotation methods for proteins, ranging from conventional to deep learning approach. Further, we selected some suitable predictors from among the reviewed tools and conducted a mini comparison of their performance using a worldwide challenge dataset. Finally, we discussed the remaining major challenges in the field, and emphasized the future directions for protein function prediction with GO.
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Registered trials
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