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Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation

In view of time delay existing in gene regulation, by using the analysis idea and methods of complex network, this paper proposes a multi-time-delay gene regulation network analysis method based on the fuzzy label propagation. The algorithm takes the relative change trend coefficient, the correlatio...

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Detalles Bibliográficos
Autores principales: Li, Haigang, Zhang, Qian, Li, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7086440/
https://www.ncbi.nlm.nih.gov/pubmed/32257084
http://dx.doi.org/10.1155/2020/2389527
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author Li, Haigang
Zhang, Qian
Li, Ming
author_facet Li, Haigang
Zhang, Qian
Li, Ming
author_sort Li, Haigang
collection PubMed
description In view of time delay existing in gene regulation, by using the analysis idea and methods of complex network, this paper proposes a multi-time-delay gene regulation network analysis method based on the fuzzy label propagation. The algorithm takes the relative change trend coefficient, the correlation coefficient, and the mutual information as the similarity measurement indexes for the gene pair, fully reflecting the correlation of gene pairs and simultaneously obtaining the gene regulation relationship and the time delay through the fuzzy label propagation algorithm of the semisupervised learning. Experimental results of the cell cycle-regulated genes of yeast show that the proposed construction method of GRN can not only correctly select potential regulation genes but also provide details about the gene regulator model, thereby more accurately constructing gene regulation network.
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spelling pubmed-70864402020-04-03 Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation Li, Haigang Zhang, Qian Li, Ming J Healthc Eng Research Article In view of time delay existing in gene regulation, by using the analysis idea and methods of complex network, this paper proposes a multi-time-delay gene regulation network analysis method based on the fuzzy label propagation. The algorithm takes the relative change trend coefficient, the correlation coefficient, and the mutual information as the similarity measurement indexes for the gene pair, fully reflecting the correlation of gene pairs and simultaneously obtaining the gene regulation relationship and the time delay through the fuzzy label propagation algorithm of the semisupervised learning. Experimental results of the cell cycle-regulated genes of yeast show that the proposed construction method of GRN can not only correctly select potential regulation genes but also provide details about the gene regulator model, thereby more accurately constructing gene regulation network. Hindawi 2020-03-11 /pmc/articles/PMC7086440/ /pubmed/32257084 http://dx.doi.org/10.1155/2020/2389527 Text en Copyright © 2020 Haigang Li et al. http://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
Li, Haigang
Zhang, Qian
Li, Ming
Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation
title Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation
title_full Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation
title_fullStr Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation
title_full_unstemmed Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation
title_short Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation
title_sort research on multi-time-delay gene regulation network based on fuzzy label propagation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7086440/
https://www.ncbi.nlm.nih.gov/pubmed/32257084
http://dx.doi.org/10.1155/2020/2389527
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