ArticlePloS one2016
Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
34 citing papers in PubMed, 62 citations in OpenAlex.
- c-MYC enhances transcription of the type 1 diabetes mellitus associated gene BATF3 via promoter binding.Scientific reports · 2026Article
- Identification and verification of feature biomarkers associated with CD14Journal of diabetes investigation · 2025Article
- In silico and functional analysis identifies key gene networks and novel gene candidates in obesity-linked human visceral fat.Obesity (Silver Spring, Md.) · 2024Article
- A gene network-driven approach to infer novel pathogenicity-associated genes: application tomSystems · 2023Article
- Identification of key regulatory genes and their working mechanisms in type 1 diabetes.BMC medical genomics · 2023Article
- The proteome and phosphoproteome of circulating extracellular vesicle-enriched preparations are associated with characteristic clinical features in type 1 diabetes.Frontiers in endocrinology · 2023Article
- Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug-gene interaction networks analysis.Scientific reports · 2022Article
- Identifying key genes in milk fat metabolism by weighted gene co-expression network analysis.Scientific reports · 2022Article
- Construction and analysis of gene co-expression network in the pathogenic fungusFrontiers in microbiology · 2022Article
- In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlyingFrontiers in microbiology · 2022Article
- Identification of Key Genes Associated With Early Calf-Hood Nutrition in Subcutaneous and Visceral Adipose Tissues by Co-Expression Analysis.Frontiers in veterinary science · 2022Article
- Dynamic changes in immune gene co-expression networks predict development of type 1 diabetes.Scientific reports · 2021Article
- Regulatory network of miRNA, lncRNA, transcription factor and target immune response genes in bovine mastitis.Scientific reports · 2021Article
- Linking Coregulated Gene Modules with Polycyclic Aromatic Hydrocarbon-Related Cancer Risk in the 3D Human Bronchial Epithelium.Chemical research in toxicology · 2021Article
- Construction and Analysis of Coexpression Network to Understand Biological Responses in Chickens Infected byFrontiers in veterinary science · 2021Article
- Transcriptome Dynamics Underlying Chlamydospore Formation inFrontiers in microbiology · 2021Article
- In silico-driven analysis of thePeerJ · 2021Article
- Combining Genome and Gene Co-expression Network Analyses for the Identification of Genes Potentially Regulating Salt Tolerance in Rice.Frontiers in plant science · 2021Article
- Computational Analysis of the Global Effects ofGenes · 2020Article
- A Novel Genes Signature Associated with the Progression of Polycystic Ovary Syndrome.Pathology oncology research : POR · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Type 1 diabetes (T1D) is a complex disease, caused by the autoimmune destruction of the insulin producing pancreatic beta cells, resulting in the body's inability to produce insulin. While great efforts have been put into understanding the genetic and environmental factors that contribute to the etiology of the disease, the exact molecular mechanisms are still largely unknown. T1D is a heterogeneous disease, and previous research in this field is mainly focused on the analysis of single genes, or using traditional gene expression profiling, which generally does not reveal the functional context of a gene associated with a complex disorder. However, network-based analysis does take into account the interactions between the diabetes specific genes or proteins and contributes to new knowledge about disease modules, which in turn can be used for identification of potential new biomarkers for T1D. In this study, we analyzed public microarray data of T1D patients and healthy controls by applying a systems biology approach that combines network-based Weighted Gene Co-Expression Network Analysis (WGCNA) with functional enrichment analysis. Novel co-expression gene network modules associated with T1D were elucidated, which in turn provided a basis for the identification of potential pathways and biomarker genes that may be involved in development of T1D.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.