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Artículo

Genome-wide pre-miRNA discovery from few labeled examples

Yones, Cristian ArielIcon ; Stegmayer, GeorginaIcon ; Milone, Diego HumbertoIcon
Fecha de publicación: 09/2017
Editorial: Oxford University Press
Revista: Bioinformatics (Oxford, England)
ISSN: 1367-4803
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Ciencias de la Computación

Resumen

MOTIVATION:Although many machine learning techniques have been proposed for distinguishing miRNA hairpins from other stem-loop sequences, most of the current methods use supervised learning, which requires a very good set of positive and negative examples. Those methods have important practical limitations when they have to be applied to a real prediction task. First, there is the challenge of dealing with a scarce number of positive (well-known) pre-miRNA examples. Secondly, it is very difficult to build a good set of negative examples for representing the full spectrum of non-miRNA sequences. Thirdly, in any genome, there is a huge class imbalance (1: 10 000) that is well-known for particularly affecting supervised classifiers.RESULTS:To enable efficient and speedy genome-wide predictions of novel miRNAs, we present miRNAss, which is a novel method based on semi-supervised learning. It takes advantage of the information provided by the unlabeled stem-loops, thereby improving the prediction rates, even when the number of labeled examples is low and not representative of the classes. An automatic method for searching negative examples to initialize the algorithm is also proposed so as to spare the user this difficult task. MiRNAss obtained better prediction rates and shorter execution times than state-of-the-art supervised methods. It was validated with genome-wide data from three model species, with more than one million of hairpin sequences each, thereby demonstrating its applicability to a real prediction task.AVAILABILITY AND IMPLEMENTATION:An R package can be downloaded from https://cran.r-project.org/package=miRNAss. In addition, a web-demo for testing the algorithm is available at http://fich.unl.edu.ar/sinc/web-demo/mirnass. All the datasets that were used in this study and the sets of predicted pre-miRNA are available on http://sourceforge.net/projects/sourcesinc/files/mirnass.
Palabras clave: Semi-Supervised Learning , Pre-Mirna Prediction , Graphs
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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/47024
URL: http://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx612
DOI: http://dx.doi.org/10.1093/bioinformatics/btx612
Colecciones
Articulos(SINC(I))
Articulos de INST. DE INVESTIGACION EN SEÑALES, SISTEMAS E INTELIGENCIA COMPUTACIONAL
Citación
Yones, Cristian Ariel; Stegmayer, Georgina; Milone, Diego Humberto; Genome-wide pre-miRNA discovery from few labeled examples; Oxford University Press; Bioinformatics (Oxford, England); 34; 4; 9-2017; 541-549
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