Improving Performance of Mammalian MicroRNA Target Prediction

Hui Liu1*, Dong Yue 2*, Yidong Chen4,5*, Shou-Jiang Gao3,5* and Yufei Huang2,5$

1SIEE, China University of Mining and Technology, Xuzhou, Jiangsu, CHINA.
2Department of ECE, University of Texas at San Antonio.3 Department of Pediatrics.4 Department of Epidemiology and Biostatistics, 5Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio.*These authors contributed equally to this work.$Corresponding author.



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.
Result: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. SMVicrO 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


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Supplementary material
SVMicrO Source Code



Prediction Result :


Contact:Yufei Huang, Department of ECE, University of Texas at San Antonio, TX 78249, USA