Review of Weighted Gene Coexpression Network Analysis

2011 ◽  
pp. 369-388 ◽  
Author(s):  
Tova Fuller ◽  
Peter Langfelder ◽  
Angela Presson ◽  
Steve Horvath
Keyword(s):  
Network Analysis ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

scholarly journals Weighted gene coexpression network analysis reveals hub genes involved in cholangiocarcinoma progression and prognosis

2019 ◽  
Vol 49 (10) ◽  
pp. 1195-1206 ◽  
Author(s):  
Aiping Tian ◽  
Ke Pu ◽  
Boxuan Li ◽  
Min Li ◽  
Xiaoguang Liu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

scholarly journals Correction to: Weighted gene coexpression network analysis-based identification of key modules and hub genes associated with drought sensitivity in rice

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Baiyang Yu ◽  
Jianbin Liu ◽  
Di Wu ◽  
Ying Liu ◽  
Weijian Cen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Drought Sensitivity ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals Identification of Candidate Genes and MicroRNAs for Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yan Li ◽  
Xiao_nan He ◽  
Chao Li ◽  
Ling Gong ◽  
Min Liu
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Microarray Data ◽  
Differentially Expressed ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Background. Identification of potential molecular targets of acute myocardial infarction is crucial to our comprehensive understanding of the disease mechanism. However, studies of gene coexpression analysis via jointing multiple microarray data of acute myocardial infarction still remain restricted. Methods. Microarray data of acute myocardial infarction (GSE48060, GSE66360, GSE97320, and GSE19339) were downloaded from Gene Expression Omnibus database. Three data sets without heterogeneity (GSE48060, GSE66360, and GSE97320) were subjected to differential expression analysis using MetaDE package. Differentially expressed genes having upper 25% variation across samples were imported in weighted gene coexpression network analysis. Functional and pathway enrichment analyses were conducted for genes in the most significant module using DAVID. The predicted microRNAs to regulate target genes in the most significant module were identified using TargetScan. Moreover, subpathway analyses using iSubpathwayMiner package and GenCLiP 2.0 were performed on hub genes with high connective weight in the most significant module. Results. A total of 1027 differentially expressed genes and 33 specific modules were screened out between acute myocardial infarction patients and control samples. Ficolin (collagen/fibrinogen domain containing) 1 (FCN1), CD14 molecule (CD14), S100 calcium binding protein A9 (S100A9), and mitochondrial aldehyde dehydrogenase 2 (ALDH2) were identified as critical target molecules; hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential regulators of the expression of the key genes in the two biggest modules. Conclusions. FCN1, CD14, S100A9, ALDH2, hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential candidate regulators in acute myocardial infarction. These findings might provide new comprehension into the underlying molecular mechanism of disease.

Early Stage Biomarkers Screening of Prostate Cancer Based on Weighted Gene Coexpression Network Analysis

2019 ◽  
Vol 38 (5) ◽  
pp. 468-475 ◽  
Author(s):  
Lingyin Meng ◽  
Yang Li ◽  
Jing Ren ◽  
Tao Shi ◽  
Jianlong Men ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Network Analysis ◽  
Early Stage ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

scholarly journals Leveraging predicted gene expression data for recapitulation of gene coexpression network analysis associations with AD pathology and cognitive decline

2020 ◽  
Vol 16 (S2) ◽  
Author(s):  
Mabel Seto ◽  
Vaibhav A. Janve ◽  
Benjamin A. Logsdon ◽  
Sara Mostafavi ◽  
Logan Dumitrescu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Cognitive Decline ◽  
Gene Expression Data ◽  
Coexpression Network ◽  
Expression Data ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Predisposition to treatment response in major depressive episode: A peripheral blood gene coexpression network analysis

2016 ◽  
Vol 81 ◽  
pp. 119-126 ◽  
Author(s):  
Raoul Belzeaux ◽  
Chien-Wei Lin ◽  
Ying Ding ◽  
Aurélie Bergon ◽  
El Chérif Ibrahim ◽  
...  
Keyword(s):  
Network Analysis ◽  
Treatment Response ◽  
Peripheral Blood ◽  
Depressive Episode ◽  
Major Depressive Episode ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Major Depressive ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

scholarly journals Identification of crucial genes in intracranial aneurysm based on weighted gene coexpression network analysis

2015 ◽  
Vol 22 (5) ◽  
pp. 238-245 ◽  
Author(s):  
X Zheng ◽  
C Xue ◽  
G Luo ◽  
Y Hu ◽  
W Luo ◽  
...  
Keyword(s):  
Network Analysis ◽  
Intracranial Aneurysm ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis
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