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GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings
BACKGROUND: Semantic similarity between Gene Ontology (GO) terms is a fundamental measure for many bioinformatics applications, such as determining functional similarity between genes or proteins. Most previous research exploited information content to estimate the semantic similarity between GO ter...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8424702/ https://www.ncbi.nlm.nih.gov/pubmed/31874639 http://dx.doi.org/10.1186/s12864-019-6272-2 |
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author | Zhong, Xiaoshi Kaalia, Rama Rajapakse, Jagath C. |
author_facet | Zhong, Xiaoshi Kaalia, Rama Rajapakse, Jagath C. |
author_sort | Zhong, Xiaoshi |
collection | PubMed |
description | BACKGROUND: Semantic similarity between Gene Ontology (GO) terms is a fundamental measure for many bioinformatics applications, such as determining functional similarity between genes or proteins. Most previous research exploited information content to estimate the semantic similarity between GO terms; recently some research exploited word embeddings to learn vector representations for GO terms from a large-scale corpus. In this paper, we proposed a novel method, named GO2Vec, that exploits graph embeddings to learn vector representations for GO terms from GO graph. GO2Vec combines the information from both GO graph and GO annotations, and its learned vectors can be applied to a variety of bioinformatics applications, such as calculating functional similarity between proteins and predicting protein-protein interactions. RESULTS: We conducted two kinds of experiments to evaluate the quality of GO2Vec: (1) functional similarity between proteins on the Collaborative Evaluation of GO-based Semantic Similarity Measures (CESSM) dataset and (2) prediction of protein-protein interactions on the Yeast and Human datasets from the STRING database. Experimental results demonstrate the effectiveness of GO2Vec over the information content-based measures and the word embedding-based measures. CONCLUSION: Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GO and GOA graphs. Our results also demonstrate that GO annotations provide useful information for computing the similarity between GO terms and between proteins. |
format | Online Article Text |
id | pubmed-8424702 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84247022021-09-10 GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings Zhong, Xiaoshi Kaalia, Rama Rajapakse, Jagath C. BMC Genomics Research BACKGROUND: Semantic similarity between Gene Ontology (GO) terms is a fundamental measure for many bioinformatics applications, such as determining functional similarity between genes or proteins. Most previous research exploited information content to estimate the semantic similarity between GO terms; recently some research exploited word embeddings to learn vector representations for GO terms from a large-scale corpus. In this paper, we proposed a novel method, named GO2Vec, that exploits graph embeddings to learn vector representations for GO terms from GO graph. GO2Vec combines the information from both GO graph and GO annotations, and its learned vectors can be applied to a variety of bioinformatics applications, such as calculating functional similarity between proteins and predicting protein-protein interactions. RESULTS: We conducted two kinds of experiments to evaluate the quality of GO2Vec: (1) functional similarity between proteins on the Collaborative Evaluation of GO-based Semantic Similarity Measures (CESSM) dataset and (2) prediction of protein-protein interactions on the Yeast and Human datasets from the STRING database. Experimental results demonstrate the effectiveness of GO2Vec over the information content-based measures and the word embedding-based measures. CONCLUSION: Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GO and GOA graphs. Our results also demonstrate that GO annotations provide useful information for computing the similarity between GO terms and between proteins. BioMed Central 2020-02-18 /pmc/articles/PMC8424702/ /pubmed/31874639 http://dx.doi.org/10.1186/s12864-019-6272-2 Text en © The Author(s) 2019 https://creativecommons.org/licenses/by/4.0/Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Zhong, Xiaoshi Kaalia, Rama Rajapakse, Jagath C. GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings |
title | GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings |
title_full | GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings |
title_fullStr | GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings |
title_full_unstemmed | GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings |
title_short | GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings |
title_sort | go2vec: transforming go terms and proteins to vector representations via graph embeddings |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8424702/ https://www.ncbi.nlm.nih.gov/pubmed/31874639 http://dx.doi.org/10.1186/s12864-019-6272-2 |
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