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A new gene regulatory network model based on BP algorithm for interrogating differentially expressed genes of Sea Urchin

BACKGROUND: Computer science and mathematical theories are combined to analyze the complex interactions among genes, which are simplified to a network to establish a theoretical model for the analysis of the structure, module and dynamic properties. In contrast, traditional model of gene regulatory...

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Detalles Bibliográficos
Autores principales: Liu, Longlong, Zhao, Tingting, Ma, Meng, Wang, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5095099/
https://www.ncbi.nlm.nih.gov/pubmed/27867818
http://dx.doi.org/10.1186/s40064-016-3526-1
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author Liu, Longlong
Zhao, Tingting
Ma, Meng
Wang, Yan
author_facet Liu, Longlong
Zhao, Tingting
Ma, Meng
Wang, Yan
author_sort Liu, Longlong
collection PubMed
description BACKGROUND: Computer science and mathematical theories are combined to analyze the complex interactions among genes, which are simplified to a network to establish a theoretical model for the analysis of the structure, module and dynamic properties. In contrast, traditional model of gene regulatory networks often lack an effective method for solving gene expression data because of high durational and spatial complexity. In this paper, we propose a new model for constructing gene regulatory networks using back propagation (BP) neural network based on predictive function and network topology. RESULTS: Combined with complex nonlinear mapping and self-learning, the BP neural network was mapped into a complex network. Network characteristics were obtained from the parameters of the average path length, average clustering coefficient, average degree, modularity, and map’s density to simulate the real gene network by an artificial network. Through the statistical analysis and comparison of network parameters of Sea Urchin mRNA microarray data under different temperatures, the value of network parameters was observed. Differentially expressed Sea Urchin genes associated with temperature were determined by calculating the difference in the degree of each gene from different networks. CONCLUSION: The new model we developed is suitable to simulate gene regulatory network and has capability of determining differentially expressed genes.
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spelling pubmed-50950992016-11-18 A new gene regulatory network model based on BP algorithm for interrogating differentially expressed genes of Sea Urchin Liu, Longlong Zhao, Tingting Ma, Meng Wang, Yan Springerplus Research BACKGROUND: Computer science and mathematical theories are combined to analyze the complex interactions among genes, which are simplified to a network to establish a theoretical model for the analysis of the structure, module and dynamic properties. In contrast, traditional model of gene regulatory networks often lack an effective method for solving gene expression data because of high durational and spatial complexity. In this paper, we propose a new model for constructing gene regulatory networks using back propagation (BP) neural network based on predictive function and network topology. RESULTS: Combined with complex nonlinear mapping and self-learning, the BP neural network was mapped into a complex network. Network characteristics were obtained from the parameters of the average path length, average clustering coefficient, average degree, modularity, and map’s density to simulate the real gene network by an artificial network. Through the statistical analysis and comparison of network parameters of Sea Urchin mRNA microarray data under different temperatures, the value of network parameters was observed. Differentially expressed Sea Urchin genes associated with temperature were determined by calculating the difference in the degree of each gene from different networks. CONCLUSION: The new model we developed is suitable to simulate gene regulatory network and has capability of determining differentially expressed genes. Springer International Publishing 2016-11-03 /pmc/articles/PMC5095099/ /pubmed/27867818 http://dx.doi.org/10.1186/s40064-016-3526-1 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Liu, Longlong
Zhao, Tingting
Ma, Meng
Wang, Yan
A new gene regulatory network model based on BP algorithm for interrogating differentially expressed genes of Sea Urchin
title A new gene regulatory network model based on BP algorithm for interrogating differentially expressed genes of Sea Urchin
title_full A new gene regulatory network model based on BP algorithm for interrogating differentially expressed genes of Sea Urchin
title_fullStr A new gene regulatory network model based on BP algorithm for interrogating differentially expressed genes of Sea Urchin
title_full_unstemmed A new gene regulatory network model based on BP algorithm for interrogating differentially expressed genes of Sea Urchin
title_short A new gene regulatory network model based on BP algorithm for interrogating differentially expressed genes of Sea Urchin
title_sort new gene regulatory network model based on bp algorithm for interrogating differentially expressed genes of sea urchin
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5095099/
https://www.ncbi.nlm.nih.gov/pubmed/27867818
http://dx.doi.org/10.1186/s40064-016-3526-1
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