ArticleBMC genomics2016
Improving microRNA target prediction with gene expression profiles.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
- Review
- Machine Learning Based Methods and Best Practices of microRNA-Target Prediction and Validation.Advances in experimental medicine and biology · 2022Article
- BRAFeLife · 2021Article
- Polymorphisms in miRNA binding sites involved in metabolic diseases in mice and humans.Scientific reports · 2020Article
- Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington's disease mice.BMC bioinformatics · 2020Article
- Shear stress regulation of miR-93 and miR-484 maturation through nucleolin.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Expression Profiling Analysis Reveals Key MicroRNA-mRNA Interactions in Early Retinal Degeneration in Retinitis Pigmentosa.Investigative ophthalmology & visual science · 2018Article
- mirDIP 4.1-integrative database of human microRNA target predictions.Nucleic acids research · 2018Article
- Review
- miR-182 Regulates Slit2-Mediated Axon Guidance by Modulating the Local Translation of a Specific mRNA.Cell reports · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMammalian genomes encode for thousands of microRNAs, which can potentially regulate the majority of protein-coding genes. They have been implicated in development and disease, leading to great interest in understanding their function, with computational methods being widely used to predict their targets. Most computational methods rely on sequence features, thermodynamics, and conservation filters; essentially scanning the whole transcriptome to predict one set of targets for each microRNA. This has the limitation of not considering that the same microRNA could have different sets of targets, and thus different functions, when expressed in different types of cells.
resultsTo address this problem, we combine popular target prediction methods with expression profiles, via machine learning, to produce a new predictor: TargetExpress. Using independent data from microarrays and high-throughput sequencing, we show that TargetExpress outperforms existing methods, and that our predictions are enriched in functions that are coherent with the added expression profile and literature reports.
conclusionsOur method should be particularly useful for anyone studying the functions and targets of miRNAs in specific tissues or cells. TargetExpress is available at: http://targetexpress.ceiabreulab.org/ .
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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.