ArticlePLoS computational biology2022
Integrating unsupervised language model with triplet neural networks for protein gene ontology prediction.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- 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
- Hybrid deep learning with protein language models and dual-path architecture for predicting IDP functions.Briefings in bioinformatics · 2026Article
- EZpred: improving deep learning-based enzyme function prediction using unlabeled sequence homologs.bioRxiv : the preprint server for biology · 2025Article
- MKFGO: integrating multi-source knowledge fusion with pretrained language model for high-accuracy protein function prediction.Briefings in bioinformatics · 2025Article
- GOBeacon: An ensemble model for protein function prediction enhanced by contrastive learning.Protein science : a publication of the Protein Society · 2025Article
- A multimodal model for protein function prediction.Scientific reports · 2025Article
- Predicting the bacterial host range of plasmid genomes using the language model-based one-class support vector machine algorithm.Microbial genomics · 2025Article
- DPFunc: accurately predicting protein function via deep learning with domain-guided structure information.Nature communications · 2025Article
- Using InterLabelGO+ for Accurate Protein Language Model-Based Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Deep learning methods for protein function prediction.Proteomics · 2025Review
- Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review.Frontiers in bioengineering and biotechnology · 2025Review
- Machine Learning for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- GOPhage: protein function annotation for bacteriophages by integrating the genomic context.Briefings in bioinformatics · 2024Article
- InterLabelGO+: unraveling label correlations in protein function prediction.Bioinformatics (Oxford, England) · 2024Article
- TAWFN: a deep learning framework for protein function prediction.Bioinformatics (Oxford, England) · 2024Article
- Processing of Short-Form Content in Clinical Narratives: Systematic Scoping Review.Journal of medical Internet research · 2024Article
- Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training.Briefings in bioinformatics · 2024Article
- PANDA-3D: protein function prediction based on AlphaFold models.NAR genomics and bioinformatics · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Accurate identification of protein function is critical to elucidate life mechanisms and design new drugs. We proposed a novel deep-learning method, ATGO, to predict Gene Ontology (GO) attributes of proteins through a triplet neural-network architecture embedded with pre-trained language models from protein sequences. The method was systematically tested on 1068 non-redundant benchmarking proteins and 3328 targets from the third Critical Assessment of Protein Function Annotation (CAFA) challenge. Experimental results showed that ATGO achieved a significant increase of the GO prediction accuracy compared to the state-of-the-art approaches in all aspects of molecular function, biological process, and cellular component. Detailed data analyses showed that the major advantage of ATGO lies in the utilization of pre-trained transformer language models which can extract discriminative functional pattern from the feature embeddings. Meanwhile, the proposed triplet network helps enhance the association of functional similarity with feature similarity in the sequence embedding space. In addition, it was found that the combination of the network scores with the complementary homology-based inferences could further improve the accuracy of the predicted models. These results demonstrated a new avenue for high-accuracy deep-learning function prediction that is applicable to large-scale protein function annotations from sequence alone.
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