Cargando…
Identification of Protein Subcellular Localization With Network and Functional Embeddings
The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein represent...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873866/ https://www.ncbi.nlm.nih.gov/pubmed/33584818 http://dx.doi.org/10.3389/fgene.2020.626500 |
_version_ | 1783649464205967360 |
---|---|
author | Pan, Xiaoyong Li, Hao Zeng, Tao Li, Zhandong Chen, Lei Huang, Tao Cai, Yu-Dong |
author_facet | Pan, Xiaoyong Li, Hao Zeng, Tao Li, Zhandong Chen, Lei Huang, Tao Cai, Yu-Dong |
author_sort | Pan, Xiaoyong |
collection | PubMed |
description | The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein–protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods. |
format | Online Article Text |
id | pubmed-7873866 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78738662021-02-11 Identification of Protein Subcellular Localization With Network and Functional Embeddings Pan, Xiaoyong Li, Hao Zeng, Tao Li, Zhandong Chen, Lei Huang, Tao Cai, Yu-Dong Front Genet Genetics The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein–protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods. Frontiers Media S.A. 2021-01-20 /pmc/articles/PMC7873866/ /pubmed/33584818 http://dx.doi.org/10.3389/fgene.2020.626500 Text en Copyright © 2021 Pan, Li, Zeng, Li, Chen, Huang and Cai. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Pan, Xiaoyong Li, Hao Zeng, Tao Li, Zhandong Chen, Lei Huang, Tao Cai, Yu-Dong Identification of Protein Subcellular Localization With Network and Functional Embeddings |
title | Identification of Protein Subcellular Localization With Network and Functional Embeddings |
title_full | Identification of Protein Subcellular Localization With Network and Functional Embeddings |
title_fullStr | Identification of Protein Subcellular Localization With Network and Functional Embeddings |
title_full_unstemmed | Identification of Protein Subcellular Localization With Network and Functional Embeddings |
title_short | Identification of Protein Subcellular Localization With Network and Functional Embeddings |
title_sort | identification of protein subcellular localization with network and functional embeddings |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873866/ https://www.ncbi.nlm.nih.gov/pubmed/33584818 http://dx.doi.org/10.3389/fgene.2020.626500 |
work_keys_str_mv | AT panxiaoyong identificationofproteinsubcellularlocalizationwithnetworkandfunctionalembeddings AT lihao identificationofproteinsubcellularlocalizationwithnetworkandfunctionalembeddings AT zengtao identificationofproteinsubcellularlocalizationwithnetworkandfunctionalembeddings AT lizhandong identificationofproteinsubcellularlocalizationwithnetworkandfunctionalembeddings AT chenlei identificationofproteinsubcellularlocalizationwithnetworkandfunctionalembeddings AT huangtao identificationofproteinsubcellularlocalizationwithnetworkandfunctionalembeddings AT caiyudong identificationofproteinsubcellularlocalizationwithnetworkandfunctionalembeddings |