ArticleBMC bioinformatics2010
Detecting microRNA activity from gene expression data.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Article
- The molecular effect of a polymorphic microRNA binding site of Wolfram syndrome 1 gene in dogs.BMC genetics · 2020Article
- Progesterone Receptor Signaling Selectively Modulates Cytokine-Induced Global Gene Expression in Human Cervical Stromal Cells.Frontiers in genetics · 2020Article
- MiRNA-BD: an evidence-based bioinformatics model and software tool for microRNA biomarker discovery.RNA biology · 2018Article
- Network Consistency Projection for Human miRNA-Disease Associations Inference.Scientific reports · 2016Article
- Transcriptome-wide based identification of miRs in congenital anomalies of the kidney and urinary tract (CAKUT) in children: the significant upregulation of tissue miR-144 expression.Journal of translational medicine · 2016Article
- Inferred miRNA activity identifies miRNA-mediated regulatory networks underlying multiple cancers.Bioinformatics (Oxford, England) · 2016Article
- Reconstruction of temporal activity of microRNAs from gene expression data in breast cancer cell line.BMC genomics · 2015Article
- Inferring condition-specific miRNA activity from matched miRNA and mRNA expression data.Bioinformatics (Oxford, England) · 2014Article
- Identification of candidate miRNA biomarkers from miRNA regulatory network with application to prostate cancer.Journal of translational medicine · 2014Article
- MicroRNAs with a role in gene regulation and in human diseases.Molecular biology reports · 2014Review
- Exploring the transcription factor activity in high-throughput gene expression data using RLQ analysis.BMC bioinformatics · 2013Article
- miRNA target enrichment analysis reveals directly active miRNAs in health and disease.Nucleic acids research · 2013Article
- Ensemble classifier based on context specific miRNA regulation modules: a new method for cancer outcome prediction.BMC bioinformatics · 2013Article
- Integrated miRNA, mRNA and protein expression analysis reveals the role of post-transcriptional regulation in controlling CHO cell growth rate.BMC genomics · 2012Article
- microRNAs targeting DEAD-box helicases are involved in salinity stress response in rice (Oryza sativa L.).BMC plant biology · 2012Article
- Gene expression changes as markers of early lapatinib response in a panel of breast cancer cell lines.Molecular cancer · 2012Article
- Computational approaches for identifying cancer miRNA expressions.Gene expression · 2012Article
- mESAdb: microRNA expression and sequence analysis database.Nucleic acids research · 2011Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions.
resultsHere we combine correspondence analysis, between group analysis and co-inertia analysis (CIA) to determine which miRNAs are associated with differences in gene expression levels in microarray data sets. Using a database of miRNA target predictions from TargetScan, TargetScanS, PicTar4way PicTar5way, and miRanda and combining these data with gene expression levels from sets of microarrays, this method produces a ranked list of miRNAs associated with a specified split in samples. We applied this to three different microarray datasets, a papillary thyroid carcinoma dataset, an in-house dataset of lipopolysaccharide treated mouse macrophages, and a multi-tissue dataset. In each case we were able to identified miRNAs of biological importance.
conclusionsWe describe a technique to integrate gene expression data and miRNA target predictions from multiple sources.
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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 OpenQuestion graph.