ArticleBMC genomics2011
Independent component and pathway-based analysis of miRNA-regulated gene expression in a model of type 1 diabetes.
Article in BMC genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 37 citations in OpenAlex.
- MicroRNAs and histone deacetylase inhibition-mediated protection against inflammatory β-cell damage.PloS one · 2018Article
- Serum microRNA Profiles Serve as Novel Biomarkers for Autoimmune Diseases.Frontiers in immunology · 2018Article
- Determining the optimal number of independent components for reproducible transcriptomic data analysis.BMC genomics · 2017Article
- T2DiACoD: A Gene Atlas of Type 2 Diabetes Mellitus Associated Complex Disorders.Scientific reports · 2017Article
- An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci.PLoS computational biology · 2017Article
- Alteration in Inflammation-related miR-146a Expression in NF-KB Signaling Pathway in Diabetic Rat Hippocampus.Advanced pharmaceutical bulletin · 2016Article
- Pathway analysis from lists of microRNAs: common pitfalls and alternative strategy.Nucleic acids research · 2015Article
- MicroRNAs: Novel Players in the Dialogue between Pancreatic Islets and Immune System in Autoimmune Diabetes.BioMed research international · 2015Review
- Identifying common and specific microRNAs expressed in peripheral blood mononuclear cell of type 1, type 2, and gestational diabetes mellitus patients.BMC research notes · 2013Article
- Gene network analysis of small molecules with autoimmune disease associated genes predicts a novel strategy for drug efficacy.Autoimmunity reviews · 2013Article
- MicroRNAs: new insights into chronic childhood diseases.BioMed research international · 2013Review
- An integrative analysis of cellular contexts, miRNAs and mRNAs reveals network clusters associated with antiestrogen-resistant breast cancer cells.BMC genomics · 2012Article
- Context-specific microRNA analysis: identification of functional microRNAs and their mRNA targets.Nucleic acids research · 2012Article
- Study on Genetic Variance of miR-541 in Type 1 Diabetes.ISRN endocrinology · 2012Article
- miRSystem: an integrated system for characterizing enriched functions and pathways of microRNA targets.PloS one · 2012Article
- MicroRNAs Involvement in Renal Pathophysiology: A Bird's Eye View.Indian journal of nephrologyReview
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral approaches have been developed for miRNA target prediction, including methods that incorporate expression profiling. However the methods are still in need of improvements due to a high false discovery rate. So far, none of the methods have used independent component analysis (ICA). Here, we developed a novel target prediction method based on ICA that incorporates both seed matching and expression profiling of miRNA and mRNA expressions. The method was applied on a cellular model of type 1 diabetes.
resultsMicroarray profiling identified eight miRNAs (miR-124/128/192/194/204/375/672/708) with differential expression. Applying ICA on the mRNA profiling data revealed five significant independent components (ICs) correlating to the experimental conditions. The five ICs also captured the miRNA expressions by explaining > 97% of their variance. By using ICA, seven of the eight miRNAs showed significant enrichment of sequence predicted targets, compared to only four miRNAs when using simple negative correlation. The ICs were enriched for miRNA targets that function in diabetes-relevant pathways e.g. type 1 and type 2 diabetes and maturity onset diabetes of the young (MODY).
conclusionsIn this study, ICA was applied as an attempt to separate the various factors that influence the mRNA expression in order to identify miRNA targets. The results suggest that ICA is better at identifying miRNA targets than negative correlation. Additionally, combining ICA and pathway analysis constitutes a means for prioritizing between the predicted miRNA targets. Applying the method on a model of type 1 diabetes resulted in identification of eight miRNAs that appear to affect pathways of relevance to disease mechanisms in diabetes.
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