ArticleScientific reports2021
Embeddings from deep learning transfer GO annotations beyond homology.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers.
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84 citing papers in PubMed.
- FANTASIA suite: a reproducible and configurable framework for embedding-based functional annotation of proteins.NAR genomics and bioinformatics · 2026Article
- Quantifying uncertainty in protein representations across models and tasks.Nature methods · 2026Article
- Compressing the collective knowledge of ESM into a single protein language model.Nature methods · 2026Article
- Intrinsic dataset features drive mutational effect prediction by protein language models.bioRxiv : the preprint server for biology · 2026Article
- Annotation of 200 Insect Genomes with BRAKER for Consistent Comparisons across Species.Scientific data · 2026Article
- Critical Assessment of Protein Intrinsic Disorder Round 3 - Predicting Disorder in the Era of Protein Language Models.Proteins · 2026Article
- FANTASIA leverages language models to decode the functional dark proteome across the animal tree of life.Communications biology · 2025Article
- Application of Protein Structure Encodings and Sequence Embeddings for Transporter Substrate Prediction.Molecules (Basel, Switzerland) · 2025Article
- Comparative genomics of the parasite Trichomonas vaginalis reveals genes involved in spillover from birds to humans.Nature communications · 2025Article
- Medium-sized protein language models perform well at transfer learning on realistic datasets.Scientific reports · 2025Article
- Connectivity and Adaptation Patterns of the Deep-Sea Ground-Forming Sponge Geodia hentscheli Across Its Entire Distribution.Molecular biology and evolution · 2025Article
- Annotating the microbial dark matter with HiFi-NN.iScience · 2025Article
- Medium-sized protein language models perform well at transfer learning on realistic datasets.bioRxiv : the preprint server for biology · 2025Article
- A systematic evaluation of the language-of-viral-escape model using multiple machine learning frameworks.Journal of the Royal Society, Interface · 2025Article
- Article
- DeepES: deep learning-based enzyme screening to identify orphan enzyme genes.Bioinformatics (Oxford, England) · 2025Article
- Prediction of inhibitory peptides against E.coli with desired MIC value.Scientific reports · 2025Article
- PIPENN-EMB ensemble net and protein embeddings generalise protein interface prediction beyond homology.Scientific reports · 2025Article
- Functional profiling of the sequence stockpile: a protein pair-based assessment of in silico prediction tools.Bioinformatics (Oxford, England) · 2025Article
- Nucleotide Transformer: building and evaluating robust foundation models for human genomics.Nature methods · 2025Article
24 more citing papers are in PubMed but not listed here.
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Knowing protein function is crucial to advance molecular and medical biology, yet experimental function annotations through the Gene Ontology (GO) exist for fewer than 0.5% of all known proteins. Computational methods bridge this sequence-annotation gap typically through homology-based annotation transfer by identifying sequence-similar proteins with known function or through prediction methods using evolutionary information. Here, we propose predicting GO terms through annotation transfer based on proximity of proteins in the SeqVec embedding rather than in sequence space. These embeddings originate from deep learned language models (LMs) for protein sequences (SeqVec) transferring the knowledge gained from predicting the next amino acid in 33 million protein sequences. Replicating the conditions of CAFA3, our method reaches an F
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