Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Kulmanov, Maxat, Hoehndorf, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
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
_version_ 1784736325568561152
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
work_keys_str_mv AT kulmanovmaxat deepgozeroimprovingproteinfunctionpredictionfromsequenceandzeroshotlearningbasedonontologyaxioms
AT hoehndorfrobert deepgozeroimprovingproteinfunctionpredictionfromsequenceandzeroshotlearningbasedonontologyaxioms