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DeepGO-SE: Protein function prediction as Approximate Semantic Entailment
Open accessPreprint — published as 10.1038/s42256-024-00795-w · Paper page
Abstract
The Gene Ontology (GO) is one of the most successful ontologies in the biological domain. GO is a formal theory with over 100,000 axioms that describe the molecular functions, biological processes, and cellular locations of proteins in three sub-ontologies. Many methods have been developed to automatically predict protein functions. However, only few of them use the background knowledge provided in the axioms of GO for knowledge-enhanced machine learning, or adjust and evaluate the model for the differences between the sub-ontologies. We have developed DeepGO-SE, a novel method which predicts GO functions from protein sequences using a pretrained large language model combined with a neuro-symbolic model that exploits GO axioms and performs protein function prediction as a form of approximate semantic entailment. We specifically evaluate DeepGO-SE on proteins that have no significant similarity with training proteins and demonstrate that DeepGO-SE can improve function prediction for those proteins.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Metadata
- Authors
- Kulmanov, M., Guzmán-Vega, F., Duek, P., Lane, L., Arold, S., & Hoehndorf, R.
- Affiliations
- King Abdullah University of Science and Technology (SA)
- King Abdullah University of Science and Technology
- University of Geneva (CH)
- Corresponding author
- Kulmanov, Maxat
- Hoehndorf, Robert
- Venue
- Nature Machine Intelligence
- Published
- 2023-09-27
- Volume
- 6
- Issue
- 2
- Pages
- 220-228
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W4391821988
- Cited by
- 129
- References
- 1
- Citations by year
- 2026: 50 · 2025: 57 · 2024: 21
- FWCI
- 17.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Machine Intelligence (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion, cc-by)
- Funders
- King Abdullah University of Science and Technology (FCC/1/1976-46-01, URF/1/4697-01-01, URF/1/5041-01-01, REI/1/5235-01-01, URF/1/4675-01-01)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Keywords
- protein function prediction, Gene Ontology, semantic entailment, large language models, prior knowledge, protein sequence classification
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