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Erratum to: Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction

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dc.contributor.author Marques, Yuri Bento
dc.contributor.author Oliveira, Alcione de Paiva
dc.contributor.author Vasconcelos, Ana Tereza Ribeiro
dc.contributor.author Cerqueira, Fabio Ribeiro
dc.date.accessioned 2019-02-26T14:41:43Z
dc.date.available 2019-02-26T14:41:43Z
dc.date.issued 2017
dc.identifier.issn 1471-2105
dc.identifier.uri http://dx.doi.org/10.1186/s12859-017-1508-0
dc.identifier.uri http://www.locus.ufv.br/handle/123456789/23716
dc.description.abstract MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool. en
dc.format pdf pt-BR
dc.language.iso eng pt-BR
dc.publisher BMC Bioinformatics pt-BR
dc.relation.ispartofseries v. 18, n. 113, p. 1, 2017 pt-BR
dc.rights Open Access pt-BR
dc.subject Pre-miRNA ab initio prediction pt-BR
dc.subject Random forest pt-BR
dc.subject Smote pt-BR
dc.subject microRNA pt-BR
dc.subject Machine learning pt-BR
dc.subject Data mining pt-BR
dc.title Erratum to: Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction en
dc.type Artigo pt-BR


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  • Artigos [62]
    Artigos Técnico-científicos na área de Informática

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