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A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization
Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein–protein interaction (PPI) networks or fus...
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/PMC8378176/ https://www.ncbi.nlm.nih.gov/pubmed/34422014 http://dx.doi.org/10.3389/fgene.2021.709660 |
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author | Zhang, Zhihong Jiang, Meiping Wu, Dongjie Zhang, Wang Yan, Wei Qu, Xilong |
author_facet | Zhang, Zhihong Jiang, Meiping Wu, Dongjie Zhang, Wang Yan, Wei Qu, Xilong |
author_sort | Zhang, Zhihong |
collection | PubMed |
description | Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein–protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false-negative and false-positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this article, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of the network, so as to avoid more noise. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, the NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network with more potential protein–protein interactions. Then, we use the PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicate that our NTMEP model has better performance in predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of the protein–protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein–protein interaction networks. |
format | Online Article Text |
id | pubmed-8378176 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83781762021-08-21 A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization Zhang, Zhihong Jiang, Meiping Wu, Dongjie Zhang, Wang Yan, Wei Qu, Xilong Front Genet Genetics Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein–protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false-negative and false-positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this article, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of the network, so as to avoid more noise. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, the NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network with more potential protein–protein interactions. Then, we use the PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicate that our NTMEP model has better performance in predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of the protein–protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein–protein interaction networks. Frontiers Media S.A. 2021-08-06 /pmc/articles/PMC8378176/ /pubmed/34422014 http://dx.doi.org/10.3389/fgene.2021.709660 Text en Copyright © 2021 Zhang, Jiang, Wu, Zhang, Yan and Qu. 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 Zhang, Zhihong Jiang, Meiping Wu, Dongjie Zhang, Wang Yan, Wei Qu, Xilong A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization |
title | A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization |
title_full | A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization |
title_fullStr | A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization |
title_full_unstemmed | A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization |
title_short | A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization |
title_sort | novel method for identifying essential proteins based on non-negative matrix tri-factorization |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378176/ https://www.ncbi.nlm.nih.gov/pubmed/34422014 http://dx.doi.org/10.3389/fgene.2021.709660 |
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