ArticleFrontiers in genetics2011
Accurate microRNA Target Prediction Using Detailed Binding Site Accessibility and Machine Learning on Proteomics Data.
Article in Frontiers in genetics, 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, 39 citations in OpenAlex.
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- MicroRNA bioinformatics in precision oncology: an integrated pipeline from NGS to AI-based target discovery.Journal of applied genetics · 2025Review
- miR-193b-365 microcluster downstream ofiScience · 2024Article
- microT-CNN: an avant-garde deep convolutional neural network unravels functional miRNA targets beyond canonical sites.Briefings in bioinformatics · 2024Article
- Review
- miR-409-3p repressesFrontiers in neuroscience · 2022Article
- Targeting the miRNA-155/TNFSF10 network restrains inflammatory response in the retina in a mouse model of Alzheimer's disease.Cell death & disease · 2021Article
- An evolutionarily acquired microRNA shapes development of mammalian cortical projections.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Article
- Temporal dynamics of miRNAs in human DLPFC and its association with miRNA dysregulation in schizophrenia.Translational psychiatry · 2019Article
- TarPmiR: a new approach for microRNA target site prediction.Bioinformatics (Oxford, England) · 2016Article
- microRNAs and Cardiac Cell Fate.Cells · 2014Review
- In silico identification of plant miRNAs in mammalian breast milk exosomes--a small step forward?PloS one · 2014Article
- mirMark: a site-level and UTR-level classifier for miRNA target prediction.Genome biology · 2014Article
- Integration of MicroRNA databases to study MicroRNAs associated with multiple sclerosis.Molecular neurobiology · 2012Article
- The repertoire and features of human platelet microRNAs.PloS one · 2012Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MicroRNAs (miRNAs) are a class of small regulatory genes regulating gene expression by targeting messenger RNA. Though computational methods for miRNA target prediction are the prevailing means to analyze their function, they still miss a large fraction of the targeted genes and additionally predict a large number of false positives. Here we introduce a novel algorithm called DIANA-microT-ANN which combines multiple novel target site features through an artificial neural network (ANN) and is trained using recently published high-throughput data measuring the change of protein levels after miRNA overexpression, providing positive and negative targeting examples. The features characterizing each miRNA recognition element include binding structure, conservation level, and a specific profile of structural accessibility. The ANN is trained to integrate the features of each recognition element along the 3'untranslated region into a targeting score, reproducing the relative repression fold change of the protein. Tested on two different sets the algorithm outperforms other widely used algorithms and also predicts a significant number of unique and reliable targets not predicted by the other methods. For 542 human miRNAs DIANA-microT-ANN predicts 120000 targets not provided by TargetScan 5.0. The algorithm is freely available at http://microrna.gr/microT-ANN.
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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.