Predicting Drug-miRNA Associations Combining SDNE with BiGRU.

It is well recognized that abnormal miRNA expression can result in drug resistance and pose a challenge to miRNA-based treatments. However, the drug-miRNA associations (DMA) are still incompletely understood. Conventional biological experiments have a high failure rate, lengthy cycle times, and expensive expenditures. Consequently, deep learning-based techniques for predicting DMA have been developed. In this work, we propose a novel method named SDNEDMA for DMA prediction that combines SDNE with BiGRU. The two-channel approach is used to combine the attribute and topological features of miRNAs and drugs. To be more precise, we first model the associations between drugs and miRNAs through the known bipartite network, and then utilize SDNE to obtain the topological features. Meanwhile, BiGRU is employed to acquire miRNA k-mer sequence features and drug ECFP fingerprints. Subsequently, both the topological and attribute features are fused jointly to form final features which is aimed to predict the association score for both them. Multiple features drugs and miRNAs are used at the same time, more information is fused, and the features are more accurate, so the prediction performance is better. The experiments show that SDNEDMA outperforms other state-of-the-art methods, yielding AUC of 0.9641 when we use 5-fold cross-validation on the ncDR dataset. SDNEDMA is additionally employed in a case study, showing how accurate and dependable it is. To sum up, the SDNEDMA has the ability to predict DMA with high accuracy and effectiveness, which is really important for drug development.