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A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites
Knowledge about protein-protein interactions is beneficial in understanding cellular mechanisms. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to the limitations of current techniques, it is still a challenging task to detect protein-pr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576272/ https://www.ncbi.nlm.nih.gov/pubmed/34764983 http://dx.doi.org/10.3389/fgene.2021.752732 |
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author | Wang, Pan Zhang, Guiyang Yu, Zu-Guo Huang, Guohua |
author_facet | Wang, Pan Zhang, Guiyang Yu, Zu-Guo Huang, Guohua |
author_sort | Wang, Pan |
collection | PubMed |
description | Knowledge about protein-protein interactions is beneficial in understanding cellular mechanisms. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to the limitations of current techniques, it is still a challenging task to detect protein-protein interaction sites. In this article, we presented a method based on deep learning and XGBoost (called DeepPPISP-XGB) for predicting protein-protein interaction sites. The deep learning model served as a feature extractor to remove redundant information from protein sequences. The Extreme Gradient Boosting algorithm was used to construct a classifier for predicting protein-protein interaction sites. The DeepPPISP-XGB achieved the following results: area under the receiver operating characteristic curve of 0.681, a recall of 0.624, and area under the precision-recall curve of 0.339, being competitive with the state-of-the-art methods. We also validated the positive role of global features in predicting protein-protein interaction sites. |
format | Online Article Text |
id | pubmed-8576272 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85762722021-11-10 A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites Wang, Pan Zhang, Guiyang Yu, Zu-Guo Huang, Guohua Front Genet Genetics Knowledge about protein-protein interactions is beneficial in understanding cellular mechanisms. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to the limitations of current techniques, it is still a challenging task to detect protein-protein interaction sites. In this article, we presented a method based on deep learning and XGBoost (called DeepPPISP-XGB) for predicting protein-protein interaction sites. The deep learning model served as a feature extractor to remove redundant information from protein sequences. The Extreme Gradient Boosting algorithm was used to construct a classifier for predicting protein-protein interaction sites. The DeepPPISP-XGB achieved the following results: area under the receiver operating characteristic curve of 0.681, a recall of 0.624, and area under the precision-recall curve of 0.339, being competitive with the state-of-the-art methods. We also validated the positive role of global features in predicting protein-protein interaction sites. Frontiers Media S.A. 2021-10-26 /pmc/articles/PMC8576272/ /pubmed/34764983 http://dx.doi.org/10.3389/fgene.2021.752732 Text en Copyright © 2021 Wang, Zhang, Yu and Huang. 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 Wang, Pan Zhang, Guiyang Yu, Zu-Guo Huang, Guohua A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites |
title | A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites |
title_full | A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites |
title_fullStr | A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites |
title_full_unstemmed | A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites |
title_short | A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites |
title_sort | deep learning and xgboost-based method for predicting protein-protein interaction sites |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576272/ https://www.ncbi.nlm.nih.gov/pubmed/34764983 http://dx.doi.org/10.3389/fgene.2021.752732 |
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