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

Overview of attention for article published in BMC Bioinformatics, December 2016
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Title
Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction
Published in
BMC Bioinformatics, December 2016
DOI 10.1186/s12859-016-1343-8
Pubmed ID
Authors

Yuri Bento Marques, Alcione de Paiva Oliveira, Ana Tereza Ribeiro Vasconcelos, Fabio Ribeiro Cerqueira

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.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 18%
Student > Bachelor 3 14%
Student > Master 3 14%
Professor 2 9%
Researcher 2 9%
Other 2 9%
Unknown 6 27%
Readers by discipline Count As %
Computer Science 8 36%
Biochemistry, Genetics and Molecular Biology 2 9%
Agricultural and Biological Sciences 2 9%
Engineering 2 9%
Medicine and Dentistry 1 5%
Other 1 5%
Unknown 6 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 April 2017.
All research outputs
#7,501,189
of 9,730,393 outputs
Outputs from BMC Bioinformatics
#3,485
of 4,145 outputs
Outputs of similar age
#186,974
of 261,678 outputs
Outputs of similar age from BMC Bioinformatics
#66
of 78 outputs
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