Evidence map›Paper›PMID 22115078›Full record

ArticleBMC bioinformatics2011

miREE: miRNA recognition elements ensemble.

Paula H Reyes-Herrera, Elisa Ficarra, Andrea Acquaviva, Enrico Macii

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Vps34 and TOR Kinases CoordinateMolecular and cellular biology · 2021
    Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Review
  10. MicroRNAs as Haematopoiesis Regulators.Advances in hematology · 2013
    Review
  11. Review
  12. Combining Supervised and Unsupervised Learning for Improved miRNA Target Prediction.IEEE/ACM transactions on computational biology and bioinformatics
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Paula H Reyes-HerreraDepartment of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 TO, Italy. phreyes@gmail.com
Elisa Ficarra
Andrea Acquaviva
Enrico Macii

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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/

Indexed as

Artificial IntelligenceRegulatory Sequences, Ribonucleic AcidAnimalsGene Expression RegulationHumansMicroRNAsRNA, MessengerSensitivity and SpecificitySoftwareSupport Vector MachineMicroRNAsRegulatory Sequences, Ribonucleic AcidRNA, Messenger

Identifiers

PMID22115078
PMCPMC3265527

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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.