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Open Access Methodology article

HuntMi: an efficient and taxon-specific approach in pre-miRNA identification

Adam Gudyś1*, Michał Wojciech Szcześniak2*, Marek Sikora13 and Izabela Makałowska2

Author Affiliations

1 Institute of Informatics, Faculty Of Automatic Control, Electronics And Computer Science, Silesian University of Technology, Gliwice, Poland

2 Laboratory of Bioinformatics, Faculty of Biology, Adam Mickiewicz University, Poznan, Poland

3 Institute of Innovative Technologies EMAG, Katowice, Poland

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BMC Bioinformatics 2013, 14:83  doi:10.1186/1471-2105-14-83

Published: 5 March 2013

Abstract

Background

Machine learning techniques are known to be a powerful way of distinguishing microRNA hairpins from pseudo hairpins and have been applied in a number of recognised miRNA search tools. However, many current methods based on machine learning suffer from some drawbacks, including not addressing the class imbalance problem properly. It may lead to overlearning the majority class and/or incorrect assessment of classification performance. Moreover, those tools are effective for a narrow range of species, usually the model ones. This study aims at improving performance of miRNA classification procedure, extending its usability and reducing computational time.

Results

We present HuntMi, a stand-alone machine learning miRNA classification tool. We developed a novel method of dealing with the class imbalance problem called ROC-select, which is based on thresholding score function produced by traditional classifiers. We also introduced new features to the data representation. Several classification algorithms in combination with ROC-select were tested and random forest was selected for the best balance between sensitivity and specificity. Reliable assessment of classification performance is guaranteed by using large, strongly imbalanced, and taxon-specific datasets in 10-fold cross-validation procedure. As a result, HuntMi achieves a considerably better performance than any other miRNA classification tool and can be applied in miRNA search experiments in a wide range of species.

Conclusions

Our results indicate that HuntMi represents an effective and flexible tool for identification of new microRNAs in animals, plants and viruses. ROC-select strategy proves to be superior to other methods of dealing with class imbalance problem and can possibly be used in other machine learning classification tasks. The HuntMi software as well as datasets used in the research are freely available at http://lemur.amu.edu.pl/share/HuntMi/ webcite.

Keywords:
MicroRNA; Random forest; Imbalanced learning; Genome analysis