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bioRxiv · 10.1101/2023.09.26.559473

DeepGO-SE: Protein function prediction as Approximate Semantic Entailment

Kulmanov, M.·Guzman-Vega, F. J.·Duek Roggli, P.·Lane, L.·Arold, S. T.·Hoehndorf, R.
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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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Kulmanov, M., Guzman-Vega, F. J., Duek Roggli, P., Lane, L., Arold, S. T., Hoehndorf, R.. 2023-09-28. DeepGO-SE: Protein function prediction as Approximate Semantic Entailment. https://doi.org/10.1101/2023.09.26.559473

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