Numerous studies have shown that microRNA (miRNA) plays important role in the study of human complex diseases. Identifying the association between miRNAs and diseases is important for improving the therapeutic level of complex diseases. However

traditional experimental is often limited to small-scale and high-cost

so computational simulation is urgently needed to quickly and effectively predict the potential miRNAs-disease associations. In this study

a new method is proposed to predict the miRNA-disease association by combining deep learning stacked automatic encoder algorithm with rotation forest classifier. This method can effectively extract high-level features that combine disease semantic similarity

miNRA functional similarity and miRNA sequence information

and accurately classify them. In the cross-validation experiment

this method achieved 90.30% prediction accuracy on the HMDD v3.0 dataset. Furthermore

we have also done case studies on Breast Neoplasms. As a result

28 of the top 30 miRNA-disease associations were confirmed. These excellent results indicate that this method is an effective tool for predicting miRNA-disease associations

and can provide highly reliable candidate miRNAs for biological experiments.