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Open Access Highly Accessed Research article

Improving performance of mammalian microRNA target prediction

Hui Liu1, Dong Yue2, Yidong Chen45, Shou-Jiang Gao35 and Yufei Huang25*

Author Affiliations

1 SIEE, China University of Mining and Technology, Xuzhou, Jiangsu, China

2 Department of ECE, University of Texas at San Antonio, USA

3 Department of Pediatrics, University of Texas Health Science Center at San Antonio, USA

4 Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, USA

5 Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, USA

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BMC Bioinformatics 2010, 11:476  doi:10.1186/1471-2105-11-476

Published: 22 September 2010

Abstract

Background

MicroRNAs (miRNAs) are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA levels. Computational prediction of miRNA targets is essential for elucidating the detailed functions of miRNA. However, the prediction specificity and sensitivity of the existing algorithms are still poor to generate meaningful, workable hypotheses for subsequent experimental testing. Constructing a richer and more reliable training data set and developing an algorithm that properly exploits this data set would be the key to improve the performance current prediction algorithms.

Results

A comprehensive training data set is constructed for mammalian miRNAs with its positive targets obtained from the most up-to-date miRNA target depository called miRecords and its negative targets derived from 20 microarray data. A new algorithm SVMicrO is developed, which assumes a 2-stage structure including a site support vector machine (SVM) followed by a UTR-SVM. SVMicrO makes prediction based on 21 optimal site features and 18 optimal UTR features, selected by training from a comprehensive collection of 113 site and 30 UTR features. Comprehensive evaluation of SVMicrO performance has been carried out on the training data, proteomics data, and immunoprecipitation (IP) pull-down data. Comparisons with some popular algorithms demonstrate consistent improvements in prediction specificity, sensitivity and precision in all tested cases. All the related materials including source code and genome-wide prediction of human targets are available at http://compgenomics.utsa.edu/svmicro.html webcite.

Conclusions

A 2-stage SVM based new miRNA target prediction algorithm called SVMicrO is developed. SVMicrO is shown to be able to achieve robust performance. It holds the promise to achieve continuing improvement whenever better training data that contain additional verified or high confidence positive targets and properly selected negative targets are available.