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DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235501/ https://www.ncbi.nlm.nih.gov/pubmed/35758802 http://dx.doi.org/10.1093/bioinformatics/btac256 |
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author | Kulmanov, Maxat Hoehndorf, Robert |
author_facet | Kulmanov, Maxat Hoehndorf, Robert |
author_sort | Kulmanov, Maxat |
collection | PubMed |
description | 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. AVAILABILITY AND IMPLEMENTATION: http://github.com/bio-ontology-research-group/deepgozero. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-9235501 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92355012022-06-29 DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms Kulmanov, Maxat Hoehndorf, Robert Bioinformatics ISCB/Ismb 2022 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. AVAILABILITY AND IMPLEMENTATION: http://github.com/bio-ontology-research-group/deepgozero. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-06-27 /pmc/articles/PMC9235501/ /pubmed/35758802 http://dx.doi.org/10.1093/bioinformatics/btac256 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | ISCB/Ismb 2022 Kulmanov, Maxat Hoehndorf, Robert DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms |
title | DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms |
title_full | DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms |
title_fullStr | DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms |
title_full_unstemmed | DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms |
title_short | DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms |
title_sort | deepgozero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms |
topic | ISCB/Ismb 2022 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235501/ https://www.ncbi.nlm.nih.gov/pubmed/35758802 http://dx.doi.org/10.1093/bioinformatics/btac256 |
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