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Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network
In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models w...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010314/ https://www.ncbi.nlm.nih.gov/pubmed/33815480 http://dx.doi.org/10.3389/fgene.2021.645932 |
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author | Meng, Zixuan Kuang, Linai Chen, Zhiping Zhang, Zhen Tan, Yihong Li, Xueyong Wang, Lei |
author_facet | Meng, Zixuan Kuang, Linai Chen, Zhiping Zhang, Zhen Tan, Yihong Li, Xueyong Wang, Lei |
author_sort | Meng, Zixuan |
collection | PubMed |
description | In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expression data of proteins with topological information extracted from the original PPI network. Simultaneously, a weighted domain-domain interaction (DDI) network is constructed based on the original PDI network. Next, through integrating the newly obtained weighted PPI network and weighted DDI network with the original PDI network, a weighted PDI network is further constructed. Then, based on topological features and biological information, including the subcellular localization and orthologous information of proteins, a novel PageRank-based iterative algorithm is designed and implemented on the newly constructed weighted PDI network to estimate the criticality of proteins. Finally, to assess the prediction performance of WPDINM, we compared it with 12 kinds of competitive measures. Experimental results show that WPDINM can achieve a predictive accuracy rate of 90.19, 81.96, 70.72, 62.04, 55.83, and 51.13% in the top 1%, top 5%, top 10%, top 15%, top 20%, and top 25% separately, which exceeds the prediction accuracy achieved by traditional state-of-the-art competing measures. Owing to the satisfactory identification effect, the WPDINM measure may contribute to the further development of key protein identification. |
format | Online Article Text |
id | pubmed-8010314 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80103142021-04-01 Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network Meng, Zixuan Kuang, Linai Chen, Zhiping Zhang, Zhen Tan, Yihong Li, Xueyong Wang, Lei Front Genet Genetics In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expression data of proteins with topological information extracted from the original PPI network. Simultaneously, a weighted domain-domain interaction (DDI) network is constructed based on the original PDI network. Next, through integrating the newly obtained weighted PPI network and weighted DDI network with the original PDI network, a weighted PDI network is further constructed. Then, based on topological features and biological information, including the subcellular localization and orthologous information of proteins, a novel PageRank-based iterative algorithm is designed and implemented on the newly constructed weighted PDI network to estimate the criticality of proteins. Finally, to assess the prediction performance of WPDINM, we compared it with 12 kinds of competitive measures. Experimental results show that WPDINM can achieve a predictive accuracy rate of 90.19, 81.96, 70.72, 62.04, 55.83, and 51.13% in the top 1%, top 5%, top 10%, top 15%, top 20%, and top 25% separately, which exceeds the prediction accuracy achieved by traditional state-of-the-art competing measures. Owing to the satisfactory identification effect, the WPDINM measure may contribute to the further development of key protein identification. Frontiers Media S.A. 2021-03-17 /pmc/articles/PMC8010314/ /pubmed/33815480 http://dx.doi.org/10.3389/fgene.2021.645932 Text en Copyright © 2021 Meng, Kuang, Chen, Zhang, Tan, Li and Wang. http://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 Meng, Zixuan Kuang, Linai Chen, Zhiping Zhang, Zhen Tan, Yihong Li, Xueyong Wang, Lei Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network |
title | Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network |
title_full | Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network |
title_fullStr | Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network |
title_full_unstemmed | Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network |
title_short | Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network |
title_sort | method for essential protein prediction based on a novel weighted protein-domain interaction network |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010314/ https://www.ncbi.nlm.nih.gov/pubmed/33815480 http://dx.doi.org/10.3389/fgene.2021.645932 |
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