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 their accuracy is still poor. In contrast, gene expression profiling from miRNA over-expression experiments can provide additional information about miRNA targets. This paper presents a Bayesian approach to integrate sequence level prediction result with expression profiling to improve the performance of miRNA target identification. The test on proteomic and IP pull-down data demonstrated better performance of the proposed approach.