Integrating Microarray Data and GRNs

2015 ◽  
pp. 137-153 ◽  
Author(s):  
L. Koumakis ◽  
G. Potamias ◽  
M. Tsiknakis ◽  
M. Zervakis ◽  
V. Moustakis
Keyword(s):  
Microarray Data

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

The tyrosine kinase TEK is differentially expressed in non-small cell lung adenocarcinomas.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
United States ◽  
Tyrosine Kinase ◽  
Lung Adenocarcinoma ◽  
Microarray Data ◽  
The United States ◽  
Small Cell ◽  
Small Cell Lung ◽  
Significant Differential Expression ◽  
Lung Cancers ◽  
Lung Adenocarcinomas

Non-small cell lung adenocarcinoma (NSCLC) is a leading cause of death in the United States and worldwide (1, 2). We mined published microarray data (3, 4, 5) to discover genes associated with NSCLC. We identified significant differential expression of the tyrosine kinase TEK in tumors from patients with NSCLC. TEK may be of relevance to the initiation, progression or maintenance of non-small cell lung cancers.

Identification of Candidate Genetic Markers and a Novel 4-genes Diagnostic Model in Osteoarthritis through Integrating Multiple Microarray Data

2020 ◽  
Vol 23 (8) ◽  
pp. 805-813
Author(s):  
Ai Jiang ◽  
Peng Xu ◽  
Zhenda Zhao ◽  
Qizhao Tan ◽  
Shang Sun ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Microarray Data ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Functional Enrichment ◽  
Joint Disease ◽  
Support Vector ◽  
Diagnostic Model ◽  
Data Sets

Background: Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown. Objective: To develop a gene signature in OA. Method: In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA. Results and Discussion: Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8). Conclusion: The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.

A Review of Gene Selection Tools in Classifying Cancer Microarray Data

2017 ◽  
Vol 12 (3) ◽  
pp. 202-212 ◽  
Author(s):  
Tham W. Shi ◽  
Wong S. Kah ◽  
Mohd S. Mohamad ◽  
Kohbalan Moorthy ◽  
Safaai Deris ◽  
...  
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Selection Tools
Sign in / Sign up

Export Citation Format

Share Document