ReviewFrontiers in genetics2014
Common features of microRNA target prediction tools.
Review in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 234 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
234 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Non-coding RNAs modulate autophagy in myocardial ischemia-reperfusion injury: a systematic review.Journal of cardiothoracic surgery · 2021Pooled it
- micro-RNAs dependent regulation of DNMT and HIF1α gene expression in thrombotic disorders.Scientific reports · 2019Pooled it
- In silico identification of microRNAs from the giant freshwater prawn Macrobrachium rosenbergii targeting white tail disease-associated viruses (MrNV and XSV).Virus genes · 2026Article
- Article
- MicroRNAs: A Social Network in Diabetic Retinopathy.Biomolecules · 2026Review
- Host-parasite cross-species regulation by Schistosoma japonicum EV-miRNA sja-miR-2a-3p limits hepatic fibrosis via BCL2 suppression in hepatic stellate cells.Veterinary research · 2026Article
- Beyond Metabolism: Pyruvate Carboxylase Acts as a Sequence-Selective Small RNA Sensor Orchestrating Antiviral Responses.bioRxiv : the preprint server for biology · 2026Article
- miRBind2 enables sequence-only prediction of miRNA binding and transcript repression.Bioinformatics (Oxford, England) · 2026Article
- Exosomal microRNAs in Periodontitis: From Molecular Footprints to Clinical Frontiers.ACS pharmacology & translational science · 2026Review
- Article
- Extracellular matrix-growth factor signalling drives the oncogenic mir-125b-2/UCK2 axis in hepatocellular carcinoma.Hereditas · 2026Article
- Article
- A Narrative Review on Integrative Bioinformatics Approaches for microRNA Research in Familial Mediterranean Fever: Current Insights and Future Directions.Health science reports · 2026Article
- A Multi-Tissue Yak (Animals : an open access journal from MDPI · 2026Article
- miRNA Target Prediction: An Overview of the Past and Current Tools.Methods in molecular biology (Clifton, N.J.) · 2026Review
- miRNAProtPred: computational prediction of human miRNA binding based on seed complementarity and thermodynamic stability.Frontiers in genetics · 2026Article
- Review
- MicroRNA bioinformatics in precision oncology: an integrated pipeline from NGS to AI-based target discovery.Journal of applied genetics · 2025Review
- Selective inhibition of rabies virus gene expression by human miRNAs: a therapeutic approach using the 7mer-m8 model.Virus genes · 2025Article
- Sequence, structure, and affinity of miR-34a binding sites determine repression efficacy.Nucleic acids research · 2025Article
174 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of miRNA targets is a critical initial step in identifying miRNA:mRNA target interactions for experimental validation. The available tools for miRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to miRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all miRNA target prediction tools, four main aspects of the miRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MiRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.
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Registered trials
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.