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A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins
There is a great deal of importance to SNARE proteins, and their absence from function can lead to a variety of diseases. The SNARE protein is known as a membrane fusion protein, and it is crucial for mediating vesicle fusion. The identification of SNARE proteins must therefore be conducted with an...
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/PMC9727185/ https://www.ncbi.nlm.nih.gov/pubmed/36506312 http://dx.doi.org/10.3389/fgene.2022.935717 |
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author | Gu, Xingyue Ding, Yijie Xiao, Pengfeng He, Tao |
author_facet | Gu, Xingyue Ding, Yijie Xiao, Pengfeng He, Tao |
author_sort | Gu, Xingyue |
collection | PubMed |
description | There is a great deal of importance to SNARE proteins, and their absence from function can lead to a variety of diseases. The SNARE protein is known as a membrane fusion protein, and it is crucial for mediating vesicle fusion. The identification of SNARE proteins must therefore be conducted with an accurate method. Through extensive experiments, we have developed a model based on graph-regularized k-local hyperplane distance nearest neighbor model (GHKNN) binary classification. In this, the model uses the physicochemical property extraction method to extract protein sequence features and the SMOTE method to upsample protein sequence features. The combination achieves the most accurate performance for identifying all protein sequences. Finally, we compare the model based on GHKNN binary classification with other classifiers and measure them using four different metrics: SN, SP, ACC, and MCC. In experiments, the model performs significantly better than other classifiers. |
format | Online Article Text |
id | pubmed-9727185 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97271852022-12-08 A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins Gu, Xingyue Ding, Yijie Xiao, Pengfeng He, Tao Front Genet Genetics There is a great deal of importance to SNARE proteins, and their absence from function can lead to a variety of diseases. The SNARE protein is known as a membrane fusion protein, and it is crucial for mediating vesicle fusion. The identification of SNARE proteins must therefore be conducted with an accurate method. Through extensive experiments, we have developed a model based on graph-regularized k-local hyperplane distance nearest neighbor model (GHKNN) binary classification. In this, the model uses the physicochemical property extraction method to extract protein sequence features and the SMOTE method to upsample protein sequence features. The combination achieves the most accurate performance for identifying all protein sequences. Finally, we compare the model based on GHKNN binary classification with other classifiers and measure them using four different metrics: SN, SP, ACC, and MCC. In experiments, the model performs significantly better than other classifiers. Frontiers Media S.A. 2022-11-23 /pmc/articles/PMC9727185/ /pubmed/36506312 http://dx.doi.org/10.3389/fgene.2022.935717 Text en Copyright © 2022 Gu, Ding, Xiao and He. 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 Gu, Xingyue Ding, Yijie Xiao, Pengfeng He, Tao A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins |
title | A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins |
title_full | A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins |
title_fullStr | A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins |
title_full_unstemmed | A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins |
title_short | A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins |
title_sort | ghknn model based on the physicochemical property extraction method to identify snare proteins |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9727185/ https://www.ncbi.nlm.nih.gov/pubmed/36506312 http://dx.doi.org/10.3389/fgene.2022.935717 |
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