Motivation: Protein 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. Results: We 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.
Bibliographical noteKAUST Repository Item: Exported on 2022-06-29
Acknowledged KAUST grant number(s): FCC/1/1976-34-01, URF/1/4355-01-01, URF/1/4675-01-01
Acknowledgements: This work has been supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [Award No. URF/1/4355-01-01, URF/1/4675-01-01 and FCC/1/1976-34-01]. We acknowledge support from the KAUST Supercomputing Laboratory.
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computational Mathematics
- Molecular Biology
- Statistics and Probability
- Computer Science Applications