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Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm
Recent progress in bioinformatics has facilitated the clarification of biological processes associated with complex diseases. Numerous methods of co-expression analysis have been proposed for use in the study of pairwise relationships among genes. In the present study, a combined network based on ge...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4840697/ https://www.ncbi.nlm.nih.gov/pubmed/27168792 http://dx.doi.org/10.3892/etm.2016.3131 |
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author | YUE, HONG YANG, BO YANG, FANG HU, XIAO-LI KONG, FAN-BIN |
author_facet | YUE, HONG YANG, BO YANG, FANG HU, XIAO-LI KONG, FAN-BIN |
author_sort | YUE, HONG |
collection | PubMed |
description | Recent progress in bioinformatics has facilitated the clarification of biological processes associated with complex diseases. Numerous methods of co-expression analysis have been proposed for use in the study of pairwise relationships among genes. In the present study, a combined network based on gene pairs was constructed following the conversion and combination of gene pair score values using a novel algorithm across multiple approaches. Three hippocampal expression profiles of patients with Alzheimer's disease (AD) and normal controls were extracted from the ArrayExpress database, and a total of 144 differentially expressed (DE) genes across multiple studies were identified by a rank product (RP) method. Five groups of co-expression gene pairs and five networks were identified and constructed using four existing methods [weighted gene co-expression network analysis (WGCNA), empirical Bayesian (EB), differentially co-expressed genes and links (DCGL), search tool for the retrieval of interacting genes/proteins database (STRING)] and a novel rank-based algorithm with combined score, respectively. Topological analysis indicated that the co-expression network constructed by the WGCNA method had the tendency to exhibit small-world characteristics, and the combined co-expression network was confirmed to be a scale-free network. Functional analysis of the co-expression gene pairs was conducted by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The co-expression gene pairs were mostly enriched in five pathways, namely proteasome, oxidative phosphorylation, Parkinson's disease, Huntington's disease and AD. This study provides a new perspective to co-expression analysis. Since different methods of analysis often present varying abilities, the novel combination algorithm may provide a more credible and robust outcome, and could be used to complement to traditional co-expression analysis. |
format | Online Article Text |
id | pubmed-4840697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-48406972016-05-10 Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm YUE, HONG YANG, BO YANG, FANG HU, XIAO-LI KONG, FAN-BIN Exp Ther Med Articles Recent progress in bioinformatics has facilitated the clarification of biological processes associated with complex diseases. Numerous methods of co-expression analysis have been proposed for use in the study of pairwise relationships among genes. In the present study, a combined network based on gene pairs was constructed following the conversion and combination of gene pair score values using a novel algorithm across multiple approaches. Three hippocampal expression profiles of patients with Alzheimer's disease (AD) and normal controls were extracted from the ArrayExpress database, and a total of 144 differentially expressed (DE) genes across multiple studies were identified by a rank product (RP) method. Five groups of co-expression gene pairs and five networks were identified and constructed using four existing methods [weighted gene co-expression network analysis (WGCNA), empirical Bayesian (EB), differentially co-expressed genes and links (DCGL), search tool for the retrieval of interacting genes/proteins database (STRING)] and a novel rank-based algorithm with combined score, respectively. Topological analysis indicated that the co-expression network constructed by the WGCNA method had the tendency to exhibit small-world characteristics, and the combined co-expression network was confirmed to be a scale-free network. Functional analysis of the co-expression gene pairs was conducted by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The co-expression gene pairs were mostly enriched in five pathways, namely proteasome, oxidative phosphorylation, Parkinson's disease, Huntington's disease and AD. This study provides a new perspective to co-expression analysis. Since different methods of analysis often present varying abilities, the novel combination algorithm may provide a more credible and robust outcome, and could be used to complement to traditional co-expression analysis. D.A. Spandidos 2016-05 2016-03-03 /pmc/articles/PMC4840697/ /pubmed/27168792 http://dx.doi.org/10.3892/etm.2016.3131 Text en Copyright: © Yue et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
spellingShingle | Articles YUE, HONG YANG, BO YANG, FANG HU, XIAO-LI KONG, FAN-BIN Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm |
title | Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm |
title_full | Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm |
title_fullStr | Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm |
title_full_unstemmed | Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm |
title_short | Co-expression network-based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm |
title_sort | co-expression network-based analysis of hippocampal expression data associated with alzheimer's disease using a novel algorithm |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4840697/ https://www.ncbi.nlm.nih.gov/pubmed/27168792 http://dx.doi.org/10.3892/etm.2016.3131 |
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