MicroRNAs (miRNAs) are single-stranded non-coding RNAs shown to plays important regulatory roles in a wide range of biological processes and diseases. Target prediction is the bottleneck to understand the function of miRNA. Therefore, 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, it's still hard for biologists to utilize the prediction result to identify miRNA targets. The mainly disadvantage of current target prediction algorithms include: 1 st , most algorithms are solely based on sequence information, 2 nd , accuracy is poor and 3 rd , the prediction results are lacking of biological meaning. A novel systems approach is proposed in this paper that integrates sequence level prediction, gene expression profiling of miRNA transfection as while as knowledge database information, which include signaling pathway and transcription factor regulation information. This systems approach would reduce the false positive rate of target prediction algorithms. More important, the prediction results of this approach will naturally embody gene regulation information, which is convictive guidance for biologist to implement subsequently identification research.