Metabolomics combined with pattern recognition and bioinformatics analysis methods for the development of pharmacodynamic biomarkers on liver fibrosis

2017 ◽  
Vol 13 (8) ◽  
pp. 1575-1583 ◽  
Author(s):  
Junwei Fang ◽  
Liping Wang ◽  
Yang Wang ◽  
Mingfeng Qiu ◽  
Yongyu Zhang
Keyword(s):  
Pattern Recognition ◽  
Network Analysis ◽  
Liver Fibrosis ◽  
Bioinformatics Analysis ◽  
Analysis Methods ◽  
Pharmacodynamic Biomarkers

Metabolomics combined with pattern recognition and network analysis maybe an attractive strategy for the pharmacodynamics biomarkers development on liver fibrosis.

Wie und wozu Werke verknüpfen? : Relationale Perspektiven auf eine Satire des frühen 18. Jahrhunderts

2019 ◽  
Vol 29 (1) ◽  
pp. 24-46
Author(s):  
TOBIAS WINNERLING
Keyword(s):  
Network Analysis ◽  
Analysis Methods ◽  
The Hague

Abstract Anhand des Beispieltextes Beschreibung des Landes der Alten und Neuern, und des zwischen ihnen entstandenen Krieges aus dem Jahr 1715 werden die Verflechtungen dreier gelehrter Journale des frühen 18. Jahrhunderts untersucht: Der Nouvelles Littéraires (Den Haag), der Neuen Zeitungen von Gelehrten Sachen (Leipzig) und des Berichts von neuen Sachen aus der gelehrten Welt (Frankfurt a.M.). Durch Rückgriff auf die netzwerkförmige Visualisierung dieser Verflechtungen wird deutlich, dass diese Beziehungen deutlich komplexer sind als bloßes Nachdrucken und die drei Journale nicht getrennt voneinander betrachtet werden können. Die mögliche Mitautorschaft Jean-Frédéric Bernards an den Nouvelles Littéraires wird kritisch geprüft.The interconnections between three early 18th century learned journals – the Nouvelles Littéraires (The Hague), the Neue Zeitungen von Gelehrten Sachen (Leipzig) and the Bericht von neuen Sachen aus der gelehrten Welt (Frankfurt) – are tested by the example of the text piece Beschreibung des Landes der Alten und Neuern, und des zwischen ihnen entstandenen Krieges. Recurring to visualization of these interconnections through network analysis methods makes clear that their pattern is far more complex than simple copying, and that the three journals must be taken into view together. A possible co-authorship of the Nouvelles Littéraires by Jean-Frédéric Bernard is critically evaluated.

scholarly journals Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis

2021 ◽  
Vol 7 ◽  
Author(s):  
Tao Yan ◽  
Shijie Zhu ◽  
Miao Zhu ◽  
Chunsheng Wang ◽  
Changfa Guo
Keyword(s):  
Atrial Fibrillation ◽  
Network Analysis ◽  
Molecular Mechanism ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Correlation Network ◽  
Hub Genes ◽  
Qrt Pcr ◽  
Gene Correlation

Background: Atrial fibrillation (AF) is the most common tachyarrhythmia in the clinic, leading to high morbidity and mortality. Although many studies on AF have been conducted, the molecular mechanism of AF has not been fully elucidated. This study was designed to explore the molecular mechanism of AF using integrative bioinformatics analysis and provide new insights into the pathophysiology of AF.Methods: The GSE115574 dataset was downloaded, and Cibersort was applied to estimate the relative expression of 22 kinds of immune cells. Differentially expressed genes (DEGs) were identified through the limma package in R language. Weighted gene correlation network analysis (WGCNA) was performed to cluster DEGs into different modules and explore relationships between modules and immune cell types. Functional enrichment analysis was performed on DEGs in the significant module, and hub genes were identified based on the protein-protein interaction (PPI) network. Hub genes were then verified using quantitative real-time polymerase chain reaction (qRT-PCR).Results: A total of 2,350 DEGs were identified and clustered into eleven modules using WGCNA. The magenta module with 246 genes was identified as the key module associated with M1 macrophages with the highest correlation coefficient. Three hub genes (CTSS, CSF2RB, and NCF2) were identified. The results verified using three other datasets and qRT-PCR demonstrated that the expression levels of these three genes in patients with AF were significantly higher than those in patients with SR, which were consistent with the bioinformatic analysis.Conclusion: Three novel genes identified using comprehensive bioinformatics analysis may play crucial roles in the pathophysiological mechanism in AF, which provide potential therapeutic targets and new insights into the treatment and early detection of AF.

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