RFMirTarget: predicting human microRNA target genes with a random forest classifier.

MicroRNAs are key regulators of eukaryotic gene expression whose fundamental role has already been identified in many cell pathways. The correct identification of miRNAs targets is still a major challenge in bioinformatics and has motivated the development of several computational methods to overcom...

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Autores principales:	Mariana R Mendoza, Guilherme C da Fonseca, Guilherme Loss-Morais, Ronnie Alves, Rogerio Margis, Ana L C Bazzan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0c749259c0834321b51f0ec8760cd92c
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id	oai:doaj.org-article:0c749259c0834321b51f0ec8760cd92c
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spelling	oai:doaj.org-article:0c749259c0834321b51f0ec8760cd92c2021-11-18T09:02:35ZRFMirTarget: predicting human microRNA target genes with a random forest classifier.1932-620310.1371/journal.pone.0070153https://doaj.org/article/0c749259c0834321b51f0ec8760cd92c2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23922946/?tool=EBIhttps://doaj.org/toc/1932-6203MicroRNAs are key regulators of eukaryotic gene expression whose fundamental role has already been identified in many cell pathways. The correct identification of miRNAs targets is still a major challenge in bioinformatics and has motivated the development of several computational methods to overcome inherent limitations of experimental analysis. Indeed, the best results reported so far in terms of specificity and sensitivity are associated to machine learning-based methods for microRNA-target prediction. Following this trend, in the current paper we discuss and explore a microRNA-target prediction method based on a random forest classifier, namely RFMirTarget. Despite its well-known robustness regarding general classifying tasks, to the best of our knowledge, random forest have not been deeply explored for the specific context of predicting microRNAs targets. Our framework first analyzes alignments between candidate microRNA-target pairs and extracts a set of structural, thermodynamics, alignment, seed and position-based features, upon which classification is performed. Experiments have shown that RFMirTarget outperforms several well-known classifiers with statistical significance, and that its performance is not impaired by the class imbalance problem or features correlation. Moreover, comparing it against other algorithms for microRNA target prediction using independent test data sets from TarBase and starBase, we observe a very promising performance, with higher sensitivity in relation to other methods. Finally, tests performed with RFMirTarget show the benefits of feature selection even for a classifier with embedded feature importance analysis, and the consistency between relevant features identified and important biological properties for effective microRNA-target gene alignment.Mariana R MendozaGuilherme C da FonsecaGuilherme Loss-MoraisRonnie AlvesRogerio MargisAna L C BazzanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 7, p e70153 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mariana R Mendoza Guilherme C da Fonseca Guilherme Loss-Morais Ronnie Alves Rogerio Margis Ana L C Bazzan RFMirTarget: predicting human microRNA target genes with a random forest classifier.
description	MicroRNAs are key regulators of eukaryotic gene expression whose fundamental role has already been identified in many cell pathways. The correct identification of miRNAs targets is still a major challenge in bioinformatics and has motivated the development of several computational methods to overcome inherent limitations of experimental analysis. Indeed, the best results reported so far in terms of specificity and sensitivity are associated to machine learning-based methods for microRNA-target prediction. Following this trend, in the current paper we discuss and explore a microRNA-target prediction method based on a random forest classifier, namely RFMirTarget. Despite its well-known robustness regarding general classifying tasks, to the best of our knowledge, random forest have not been deeply explored for the specific context of predicting microRNAs targets. Our framework first analyzes alignments between candidate microRNA-target pairs and extracts a set of structural, thermodynamics, alignment, seed and position-based features, upon which classification is performed. Experiments have shown that RFMirTarget outperforms several well-known classifiers with statistical significance, and that its performance is not impaired by the class imbalance problem or features correlation. Moreover, comparing it against other algorithms for microRNA target prediction using independent test data sets from TarBase and starBase, we observe a very promising performance, with higher sensitivity in relation to other methods. Finally, tests performed with RFMirTarget show the benefits of feature selection even for a classifier with embedded feature importance analysis, and the consistency between relevant features identified and important biological properties for effective microRNA-target gene alignment.
format	article
author	Mariana R Mendoza Guilherme C da Fonseca Guilherme Loss-Morais Ronnie Alves Rogerio Margis Ana L C Bazzan
author_facet	Mariana R Mendoza Guilherme C da Fonseca Guilherme Loss-Morais Ronnie Alves Rogerio Margis Ana L C Bazzan
author_sort	Mariana R Mendoza
title	RFMirTarget: predicting human microRNA target genes with a random forest classifier.
title_short	RFMirTarget: predicting human microRNA target genes with a random forest classifier.
title_full	RFMirTarget: predicting human microRNA target genes with a random forest classifier.
title_fullStr	RFMirTarget: predicting human microRNA target genes with a random forest classifier.
title_full_unstemmed	RFMirTarget: predicting human microRNA target genes with a random forest classifier.
title_sort	rfmirtarget: predicting human microrna target genes with a random forest classifier.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/0c749259c0834321b51f0ec8760cd92c
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RFMirTarget: predicting human microRNA target genes with a random forest classifier.

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