MFIDMA: A Multiple Information Integration Model for the Prediction of Drug–miRNA Associations

SIMPLE SUMMARY: Predicting the possible associations between drugs and miRNAs would provide new perspectives on miRNA therapeutics research and drug discovery. However, considering the time investment and expensive cost of wet experiments, there is an urgent need for a computational approach that would allow researchers to identify potential associations between drugs and miRNAs for further research. In this paper, we present a computational method in this field named MFIDMA for simplifying the screening process. We also collect high-quality datasets from the current database. We conduct experiments on the collected datasets to prove the excellent performance of the proposed model. The MFIDMA is intended to be useful for the prediction of associations between drugs and miRNAs, and to be effective for the development and research of miRNA-targeted drugs. ABSTRACT: Abnormal microRNA (miRNA) functions play significant roles in various pathological processes. Thus, predicting drug–miRNA associations (DMA) may hold great promise for identifying the potential targets of drugs. However, discovering the associations between drugs and miRNAs through wet experiments is time-consuming and laborious. Therefore, it is significant to develop computational prediction methods to improve the efficiency of identifying DMA on a large scale. In this paper, a multiple features integration model (MFIDMA) is proposed to predict drug–miRNA association. Specifically, we first formulated known DMA as a bipartite graph and utilized structural deep network embedding (SDNE) to learn the topological features from the graph. Second, the Word2vec algorithm was utilized to construct the attribute features of the miRNAs and drugs. Third, two kinds of features were entered into the convolution neural network (CNN) and deep neural network (DNN) to integrate features and predict potential target miRNAs for the drugs. To evaluate the MFIDMA model, it was implemented on three different datasets under a five-fold cross-validation and achieved average AUCs of 0.9407, 0.9444 and 0.8919. In addition, the MFIDMA model showed reliable results in the case studies of Verapamil and hsa-let-7c-5p, confirming that the proposed model can also predict DMA in real-world situations. The model was effective in analyzing the neighbors and topological features of the drug–miRNA network by SDNE. The experimental results indicated that the MFIDMA is an accurate and robust model for predicting potential DMA, which is significant for miRNA therapeutics research and drug discovery.