scholarly journals On the Society of Genome: Social Affiliation Network Analysis of Microarray Data

2006 ◽  
pp. 1062-1071 ◽  
Author(s):  
Jung Hun Ohn ◽  
Jihoon Kim ◽  
Ju Han Kim
Keyword(s):  
Network Analysis ◽  
Microarray Data ◽  
Social Affiliation ◽  
Affiliation Network

Expression and network analysis of Illumina microarray data

2009 ◽  
Author(s):  
Giovanni Coppola ◽  
Kellen Winden ◽  
Genevieve Konopka ◽  
Fuying Gao ◽  
Daniel Geschwind
Keyword(s):  
Network Analysis ◽  
Microarray Data

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.

Use of Social Network Analysis to Create and Foster Interdisciplinary Research, Projects, and Grants among Faculty

2012 ◽  
pp. 155-167
Author(s):  
Kyle Christensen ◽  
Iris M. Saltiel
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Interdisciplinary Research ◽  
Resource Sharing ◽  
Research Projects ◽  
Faculty Collaboration ◽  
Affiliation Network ◽  
Recommendations For Practice

This chapter describes one university-based social network created for peer mentoring, knowledge brokering, and resource sharing for faculty and students to collaborate to increase research and scholarship. First, the case study describes the process utilized to survey interests of faculty as the basis for an affiliation network of faculty research interests. The benefits of social network analysis and its applications are discussed and utilized. Next, a conversation is presented about the use of social network analysis to foster faculty collaboration through targeted programming. Finally, recommendations for practice are presented.

Network Analysis of Microarray Data

2021 ◽  
pp. 161-186
Author(s):  
Alisa Pavel ◽  
Angela Serra ◽  
Luca Cattelani ◽  
Antonio Federico ◽  
Dario Greco
Keyword(s):  
Network Analysis ◽  
Microarray Data

scholarly journals Elucidating functional context within microarray data by integrated transcription factor-focused gene-interaction and regulatory network analysis

2013 ◽  
Vol 24 (2) ◽  
pp. 75-90 ◽  
Author(s):  
Thomas Werner ◽  
Susan M. Dombrowski ◽  
Carlos Zgheib ◽  
Fouad A. Zouein ◽  
Henry L. Keen ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Network Analysis ◽  
Microarray Data ◽  
Regulatory Network ◽  
Gene Interaction ◽  
Functional Context
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