ArticleGenes2022
miRBind: A Deep Learning Method for miRNA Binding Classification.
Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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.
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Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- miRBind2 enables sequence-only prediction of miRNA binding and transcript repression.Bioinformatics (Oxford, England) · 2026Article
- DeepExoMir: A Reproducible RNA Language Model Framework for CLIP-Seq-Supported MicroRNA Target-Site Prioritization.International journal of molecular sciences · 2026Article
- rbpCNN: a biophysics-informed deep learning model for predicting piRNA and mRNA interactions.Scientific reports · 2026Article
- miRBench: novel benchmark datasets for microRNA binding site prediction that mitigate against prevalent microRNA frequency class bias.Bioinformatics (Oxford, England) · 2025Article
- The Role of microRNAs in Lung Cancer: Mechanisms, Diagnostics and Therapeutic Potential.International journal of molecular sciences · 2025Review
- Regulatory QTLs affecting miRNA-mRNA interactions in cancer: mechanisms, methods, and clinical implications.Frontiers in molecular biosciences · 2025Review
- MiRNAs as potential therapeutic targets and biomarkers for non-traumatic intracerebral hemorrhage.Biomarker research · 2024Review
- Exploring miRNA-target gene pair detection in disease with coRmiT.Briefings in bioinformatics · 2024Article
- Analysis of chimeric reads characterises the diverse targetome of AGO2-mediated regulation.Scientific reports · 2023Article
- Using Attribution Sequence Alignment to Interpret Deep Learning Models for miRNA Binding Site Prediction.Biology · 2023Article
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The binding of microRNAs (miRNAs) to their target sites is a complex process, mediated by the Argonaute (Ago) family of proteins. The prediction of miRNA:target site binding is an important first step for any miRNA target prediction algorithm. To date, the potential for miRNA:target site binding is evaluated using either co-folding free energy measures or heuristic approaches, based on the identification of binding 'seeds', i.e., continuous stretches of binding corresponding to specific parts of the miRNA. The limitations of both these families of methods have produced generations of miRNA target prediction algorithms that are primarily focused on 'canonical' seed targets, even though unbiased experimental methods have shown that only approximately half of in vivo miRNA targets are 'canonical'. Herein, we present miRBind, a deep learning method and web server that can be used to accurately predict the potential of miRNA:target site binding. We trained our method using seed-agnostic experimental data and show that our method outperforms both seed-based approaches and co-fold free energy approaches. The full code for the development of miRBind and a freely accessible web server are freely available.
Indexed as
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What OpenQuestion holds
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.