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Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features
Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769816/ https://www.ncbi.nlm.nih.gov/pubmed/35069791 http://dx.doi.org/10.1155/2022/7493834 |
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author | Liu, Mujiexin Chen, Hui Gao, Dong Ma, Cai-Yi Zhang, Zhao-Yue |
author_facet | Liu, Mujiexin Chen, Hui Gao, Dong Ma, Cai-Yi Zhang, Zhao-Yue |
author_sort | Liu, Mujiexin |
collection | PubMed |
description | Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict the uncharacterized membrane proteins of H. pylori. In this study, a reliable benchmark dataset consisted of 114 membrane and 219 nonmembrane proteins was constructed based on UniProt. A support vector machine- (SVM-) based model was developed for discriminating H. pylori membrane proteins from nonmembrane proteins by using sequence information. Cross-validation showed that our method achieved good performance with an accuracy of 91.29%. It is anticipated that the proposed model will be useful for the annotation of H. pylori membrane proteins and the development of new anti-H. pylori agents. |
format | Online Article Text |
id | pubmed-8769816 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-87698162022-01-20 Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features Liu, Mujiexin Chen, Hui Gao, Dong Ma, Cai-Yi Zhang, Zhao-Yue Comput Math Methods Med Research Article Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict the uncharacterized membrane proteins of H. pylori. In this study, a reliable benchmark dataset consisted of 114 membrane and 219 nonmembrane proteins was constructed based on UniProt. A support vector machine- (SVM-) based model was developed for discriminating H. pylori membrane proteins from nonmembrane proteins by using sequence information. Cross-validation showed that our method achieved good performance with an accuracy of 91.29%. It is anticipated that the proposed model will be useful for the annotation of H. pylori membrane proteins and the development of new anti-H. pylori agents. Hindawi 2022-01-12 /pmc/articles/PMC8769816/ /pubmed/35069791 http://dx.doi.org/10.1155/2022/7493834 Text en Copyright © 2022 Mujiexin Liu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Liu, Mujiexin Chen, Hui Gao, Dong Ma, Cai-Yi Zhang, Zhao-Yue Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features |
title | Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features |
title_full | Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features |
title_fullStr | Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features |
title_full_unstemmed | Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features |
title_short | Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features |
title_sort | identification of helicobacter pylori membrane proteins using sequence-based features |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769816/ https://www.ncbi.nlm.nih.gov/pubmed/35069791 http://dx.doi.org/10.1155/2022/7493834 |
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