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This article is part of the supplement: The ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS)

Open Access Open Badges Research

A Bayesian approach for identifying miRNA targets by combining sequence prediction and gene expression profiling

Hui Liu1, Dong Yue2, Lin Zhang1, Yidong Chen45, Shou-Jiang Gao34 and Yufei Huang25*

Author affiliations

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

2 Department of Electrical and Computer Engineering, University of Texas at San Antonio

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

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

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

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Citation and License

BMC Genomics 2010, 11(Suppl 3):S12  doi:10.1186/1471-2164-11-S3-S12

Published: 1 December 2010



MicroRNAs (miRNAs) are single-stranded non-coding RNAs shown to plays important regulatory roles in a wide range of biological processes and diseases. The functions and regulatory mechanisms of most of miRNAs are still poorly understood in part because of the difficulty in identifying the miRNA regulatory targets. To this end, computational methods have evolved as important tools for genome-wide target screening. Although considerable work in the past few years has produced many target prediction algorithms, most of them are solely based on sequence, and the accuracy is still poor. In contrast, gene expression profiling from miRNA transfection experiments can provide additional information about miRNA targets. However, most of existing research assumes down-regulated mRNAs as targets. Given the fact that the primary function of miRNA is protein inhibition, this assumption is neither sufficient nor necessary.


A novel Bayesian approach is proposed in this paper that integrates sequence level prediction with expression profiling of miRNA transfection. This approach does not restrict the target to be down-expressed and thus improve the performance of existing target prediction algorithm. The proposed algorithm was tested on simulated data, proteomics data, and IP pull-down data and shown to achieve better performance than existing approaches for target prediction. All the related materials including source code are available at webcite.


The proposed Bayesian algorithm integrates properly the sequence paring data and mRNA expression profiles for miRNA target prediction. This algorithm is shown to have better prediction performance than existing algorithms.