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Protein functional module identification method combining topological features and gene expression data
BACKGROUND: The study of protein complexes and protein functional modules has become an important method to further understand the mechanism and organization of life activities. The clustering algorithms used to analyze the information contained in protein-protein interaction network are effective w...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8185953/ https://www.ncbi.nlm.nih.gov/pubmed/34103008 http://dx.doi.org/10.1186/s12864-021-07620-3 |
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author | Zhao, Zihao Xu, Wenjun Chen, Aiwen Han, Yueyue Xia, Shengrong Xiang, ChuLei Wang, Chao Jiao, Jun Wang, Hui Yuan, Xiaohui Gu, Lichuan |
author_facet | Zhao, Zihao Xu, Wenjun Chen, Aiwen Han, Yueyue Xia, Shengrong Xiang, ChuLei Wang, Chao Jiao, Jun Wang, Hui Yuan, Xiaohui Gu, Lichuan |
author_sort | Zhao, Zihao |
collection | PubMed |
description | BACKGROUND: The study of protein complexes and protein functional modules has become an important method to further understand the mechanism and organization of life activities. The clustering algorithms used to analyze the information contained in protein-protein interaction network are effective ways to explore the characteristics of protein functional modules. RESULTS: This paper conducts an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. CONCLUSIONS: The algorithm can effectively remove the noise data reflected by calculating the topological structure characteristic values in the PPI network through the similarity of gene expression patterns, and also properly use the information hidden in the gene expression data. The experimental results show that the ECTG algorithm can detect protein functional modules better. |
format | Online Article Text |
id | pubmed-8185953 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-81859532021-06-09 Protein functional module identification method combining topological features and gene expression data Zhao, Zihao Xu, Wenjun Chen, Aiwen Han, Yueyue Xia, Shengrong Xiang, ChuLei Wang, Chao Jiao, Jun Wang, Hui Yuan, Xiaohui Gu, Lichuan BMC Genomics Research BACKGROUND: The study of protein complexes and protein functional modules has become an important method to further understand the mechanism and organization of life activities. The clustering algorithms used to analyze the information contained in protein-protein interaction network are effective ways to explore the characteristics of protein functional modules. RESULTS: This paper conducts an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. CONCLUSIONS: The algorithm can effectively remove the noise data reflected by calculating the topological structure characteristic values in the PPI network through the similarity of gene expression patterns, and also properly use the information hidden in the gene expression data. The experimental results show that the ECTG algorithm can detect protein functional modules better. BioMed Central 2021-06-08 /pmc/articles/PMC8185953/ /pubmed/34103008 http://dx.doi.org/10.1186/s12864-021-07620-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zhao, Zihao Xu, Wenjun Chen, Aiwen Han, Yueyue Xia, Shengrong Xiang, ChuLei Wang, Chao Jiao, Jun Wang, Hui Yuan, Xiaohui Gu, Lichuan Protein functional module identification method combining topological features and gene expression data |
title | Protein functional module identification method combining topological features and gene expression data |
title_full | Protein functional module identification method combining topological features and gene expression data |
title_fullStr | Protein functional module identification method combining topological features and gene expression data |
title_full_unstemmed | Protein functional module identification method combining topological features and gene expression data |
title_short | Protein functional module identification method combining topological features and gene expression data |
title_sort | protein functional module identification method combining topological features and gene expression data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8185953/ https://www.ncbi.nlm.nih.gov/pubmed/34103008 http://dx.doi.org/10.1186/s12864-021-07620-3 |
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