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Identifying Functions of Proteins in Mice With Functional Embedding Features
In current biology, exploring the biological functions of proteins is important. Given the large number of proteins in some organisms, exploring their functions one by one through traditional experiments is impossible. Therefore, developing quick and reliable methods for identifying protein function...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149260/ https://www.ncbi.nlm.nih.gov/pubmed/35651937 http://dx.doi.org/10.3389/fgene.2022.909040 |
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author | Li, Hao Zhang, ShiQi Chen, Lei Pan, Xiaoyong Li, ZhanDong Huang, Tao Cai, Yu-Dong |
author_facet | Li, Hao Zhang, ShiQi Chen, Lei Pan, Xiaoyong Li, ZhanDong Huang, Tao Cai, Yu-Dong |
author_sort | Li, Hao |
collection | PubMed |
description | In current biology, exploring the biological functions of proteins is important. Given the large number of proteins in some organisms, exploring their functions one by one through traditional experiments is impossible. Therefore, developing quick and reliable methods for identifying protein functions is necessary. Considerable accumulation of protein knowledge and recent developments on computer science provide an alternative way to complete this task, that is, designing computational methods. Several efforts have been made in this field. Most previous methods have adopted the protein sequence features or directly used the linkage from a protein–protein interaction (PPI) network. In this study, we proposed some novel multi-label classifiers, which adopted new embedding features to represent proteins. These features were derived from functional domains and a PPI network via word embedding and network embedding, respectively. The minimum redundancy maximum relevance method was used to assess the features, generating a feature list. Incremental feature selection, incorporating RAndom k-labELsets to construct multi-label classifiers, used such list to construct two optimum classifiers, corresponding to two key measurements: accuracy and exact match. These two classifiers had good performance, and they were superior to classifiers that used features extracted by traditional methods. |
format | Online Article Text |
id | pubmed-9149260 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91492602022-05-31 Identifying Functions of Proteins in Mice With Functional Embedding Features Li, Hao Zhang, ShiQi Chen, Lei Pan, Xiaoyong Li, ZhanDong Huang, Tao Cai, Yu-Dong Front Genet Genetics In current biology, exploring the biological functions of proteins is important. Given the large number of proteins in some organisms, exploring their functions one by one through traditional experiments is impossible. Therefore, developing quick and reliable methods for identifying protein functions is necessary. Considerable accumulation of protein knowledge and recent developments on computer science provide an alternative way to complete this task, that is, designing computational methods. Several efforts have been made in this field. Most previous methods have adopted the protein sequence features or directly used the linkage from a protein–protein interaction (PPI) network. In this study, we proposed some novel multi-label classifiers, which adopted new embedding features to represent proteins. These features were derived from functional domains and a PPI network via word embedding and network embedding, respectively. The minimum redundancy maximum relevance method was used to assess the features, generating a feature list. Incremental feature selection, incorporating RAndom k-labELsets to construct multi-label classifiers, used such list to construct two optimum classifiers, corresponding to two key measurements: accuracy and exact match. These two classifiers had good performance, and they were superior to classifiers that used features extracted by traditional methods. Frontiers Media S.A. 2022-05-16 /pmc/articles/PMC9149260/ /pubmed/35651937 http://dx.doi.org/10.3389/fgene.2022.909040 Text en Copyright © 2022 Li, Zhang, Chen, Pan, Li, Huang and Cai. https://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 Li, Hao Zhang, ShiQi Chen, Lei Pan, Xiaoyong Li, ZhanDong Huang, Tao Cai, Yu-Dong Identifying Functions of Proteins in Mice With Functional Embedding Features |
title | Identifying Functions of Proteins in Mice With Functional Embedding Features |
title_full | Identifying Functions of Proteins in Mice With Functional Embedding Features |
title_fullStr | Identifying Functions of Proteins in Mice With Functional Embedding Features |
title_full_unstemmed | Identifying Functions of Proteins in Mice With Functional Embedding Features |
title_short | Identifying Functions of Proteins in Mice With Functional Embedding Features |
title_sort | identifying functions of proteins in mice with functional embedding features |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149260/ https://www.ncbi.nlm.nih.gov/pubmed/35651937 http://dx.doi.org/10.3389/fgene.2022.909040 |
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