ArticleBioinformatics (Oxford, England)2022
DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- Problems With GO Enrichment as an Endpoint for Ecological Omics Interpretation.Molecular ecology · 2026Article
- DeepKOALA: a scalable deep learning framework for KEGG Orthology assignment.Briefings in bioinformatics · 2026Article
- On the state of protein function prediction: a report on the fourth CAFA challenge.bioRxiv : the preprint server for biology · 2026Article
- A unified multimodal model for generalizable zero-shot and supervised protein function prediction.Bioinformatics (Oxford, England) · 2026Article
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- MZSGO: multimodal zero-shot protein function annotation via evolutionary signals and textual semantics.Bioinformatics (Oxford, England) · 2026Article
- Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- STAR-GO: improving protein function prediction by learning to hierarchically integrate ontology-informed semantic embeddings.Bioinformatics (Oxford, England) · 2026Article
- MAGIN-GO: Protein function prediction based on dual graph neural networks and gene ontology structure.PloS one · 2026Article
- S2DA-GO: enhancing protein function prediction via gradient-decoupled cross-attention and semantic priors.Frontiers in genetics · 2026Article
- A scalable equivariant graph network framework for precise protein function prediction.Genome biology · 2025Article
- Box embeddings for extending ontologies: a data-driven and interpretable approach.Journal of cheminformatics · 2025Article
- MKFGO: integrating multi-source knowledge fusion with pretrained language model for high-accuracy protein function prediction.Briefings in bioinformatics · 2025Article
- GOAnnotator: accurate protein function annotation using automatically retrieved literature.Bioinformatics (Oxford, England) · 2025Article
- ProtNote: a multimodal method for protein-function annotation.Bioinformatics (Oxford, England) · 2025Article
- Out of distribution learning in bioinformatics: advancements and challenges.Briefings in bioinformatics · 2025Review
- Learning maximally spanning representations improves protein function annotation.bioRxiv : the preprint server for biology · 2025Article
- Machine Learning for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
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2 authors.
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Abstract
motivationProtein functions are often described using the Gene Ontology (GO) which is an ontology consisting of over 50 000 classes and a large set of formal axioms. Predicting the functions of proteins is one of the key challenges in computational biology and a variety of machine learning methods have been developed for this purpose. However, these methods usually require a significant amount of training data and cannot make predictions for GO classes that have only few or no experimental annotations.
resultsWe developed DeepGOZero, a machine learning model which improves predictions for functions with no or only a small number of annotations. To achieve this goal, we rely on a model-theoretic approach for learning ontology embeddings and combine it with neural networks for protein function prediction. DeepGOZero can exploit formal axioms in the GO to make zero-shot predictions, i.e., predict protein functions even if not a single protein in the training phase was associated with that function. Furthermore, the zero-shot prediction method employed by DeepGOZero is generic and can be applied whenever associations with ontology classes need to be predicted. AVAILABILITY AND IMPLEMENTATION: http://github.com/bio-ontology-research-group/deepgozero. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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