ArticleBMC bioinformatics2011
miREE: miRNA recognition elements ensemble.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- AAV-delivered miRNA-gated Cre circuits enable programmable gene activation in AFP-active liver tumor models.Experimental hematology & oncology · 2026Article
- Review
- Machine Learning Based Methods and Best Practices of microRNA-Target Prediction and Validation.Advances in experimental medicine and biology · 2022Article
- Vps34 and TOR Kinases CoordinateMolecular and cellular biology · 2021Article
- Prediction methods for microRNA targets in bilaterian animals: Toward a better understanding by biologists.Computational and structural biotechnology journal · 2021Review
- mirDIP 4.1-integrative database of human microRNA target predictions.Nucleic acids research · 2018Article
- In silico identification of conserved miRNAs and their selective target gene prediction in indicine (Bos indicus) cattle.PloS one · 2018Article
- miRNAs target databases: developmental methods and target identification techniques with functional annotations.Cellular and molecular life sciences : CMLS · 2017Review
- Big effects of small RNAs: a review of microRNAs in anxiety.Molecular neurobiology · 2013Review
- MicroRNAs as Haematopoiesis Regulators.Advances in hematology · 2013Review
- One decade of development and evolution of microRNA target prediction algorithms.Genomics, proteomics & bioinformatics · 2012Review
- Combining Supervised and Unsupervised Learning for Improved miRNA Target Prediction.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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Authors and funding
4 authors.
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Abstract
backgroundComputational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complementary but integrated roles in the prediction. The Ab-Initio module leverages upon a genetic algorithmic approach to generate a set of candidate sites on the basis of their microRNA-mRNA duplex stability properties. Then, a Support Vector Machine (SVM) learning module evaluates the impact of microRNA recognition elements on the target gene. As a result the prediction takes into account information regarding both miRNA-target structural stability and accessibility.
resultsThe proposed method significantly improves the state-of-the-art prediction tools in terms of accuracy with a better balance between specificity and sensitivity, as demonstrated by the experiments conducted on several large datasets across different species. miREE achieves this result by tackling two of the main challenges of current prediction tools: (1) The reduced number of false positives for the Ab-Initio part thanks to the integration of a machine learning module (2) the specificity of the machine learning part, obtained through an innovative technique for rich and representative negative records generation. The validation was conducted on experimental datasets where the miRNA:mRNA interactions had been obtained through (1) direct validation where even the binding site is provided, or through (2) indirect validation, based on gene expression variations obtained from high-throughput experiments where the specific interaction is not validated in detail and consequently the specific binding site is not provided.
conclusionsThe coupling of two parts: a sensitive Ab-Initio module and a selective machine learning part capable of recognizing the false positives, leads to an improved balance between sensitivity and specificity. miREE obtains a reasonable trade-off between filtering false positives and identifying targets. miREE tool is available online at http://didattica-online.polito.it/eda/miREE/
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