Abstract Background and Objectives Diabetic kidney disease (DKD) is one of the most common microvascular complications diabetes. It has always been difficult to explore novel biomarkers therapeutic targets DKD. We aimed identify new further their functions in Methods The weighted gene co-expression network analysis (WGCNA) method was used analyze expression profile data DKD, obtain key modules ...

Background: Atrial fibrillation (AF) is the most common complication in patients undergoing cardiac surgery. However, pathogenesis of postoperative AF (POAF) elusive, and research related to this topic sparse. Our study aimed identify key gene modules genes conduct a circular RNA (circRNA)-microRNA (miRNA)-messenger (mRNA) regulatory network analysis POAF on basis bioinformatic analysis.

Diabetic peripheral neuropathy (DPN) is a common complication of diabetes mellitus. Recent studies have explored the involvement long non-coding RNA (lncRNA) in diabetes, but its specific role DPN development remains unclear. To investigate association between lncRNAs and DPN, we conducted comprehensive bioinformatics analyses. We obtained gene expression data from sciatic nerve 8-week-old 24-w...

The objective of this paper was to identify hub genes and pathways associated with retinoblastoma using centrality analysis of the co-expression network and pathway-enrichment analysis. The co-expression network of retinoblastoma was constructed by weighted gene co-expression network analysis (WGCNA) based on differentially expressed (DE) genes, and clusters were obtained through the molecular ...

We used gene co-expression network analysis to functionally annotate long noncoding RNAs (lncRNAs) and identify their potential cancer associations. The integrated microarray data set from our previous study was used to extract the expression profiles of 1,865 lncRNAs. Known cancer genes were compiled from the Catalogue of Somatic Mutations in Cancer and UniProt databases. Co-expression analysi...

Integrated weighted correlation network analysis of mouse liver gene expression data Chapter 12 and this R software tutorial describe a case study for carrying out an integrated weighted correlation network analysis of mouse gene expression, sample trait, and genetic marker data. It describes how to i) use sample networks (signed correlation networks) for detecting outlying observations, ii) fi...

Network-based systems biology has become an important method for analyzing high-throughput gene expression data and gene function mining. Yeast has long been a popular model organism for biomedical research. In the current study, a weighted gene co-expression network analysis algorithm was applied to construct a gene co-expression network in Saccharomyces cerevisiae. Seventeen stable gene co-ex...