Inferring Drug-miRNA Associations by Integrating Drug SMILES and MiRNA Sequence Information

The accumulated evidences indicate that drugs not only interact with proteins, but also regulate a wide variety of biomarkers such as miRNAs. Hence, uncovering potential drug-miRNA associations plays significant roles in disease prevention, diagnosis and treatment as well as drug development. In this paper, we discuss how this problem is formulated as a link prediction task in a bipartite graph and construct a computational model to infer unknown drug-miRNA associations. Specifically, the drug SMILES (Simplified molecular input line entry specification) or miRNA sequences can be regarded as a kind of biology language described by distributed representation. The experiment verified associations are treated as positive samples and the same number unlabeled associations are randomly selected as negative samples. Finally, Random Forest classifier is applied to perform the prediction task. In the experiment, the proposed method achieves AUROC of 91.16 and AUPR of 89.21 under 5-fold cross-validation. It demonstrates the great potential of seamless integration of deep learning and biological big data. We hope that this research with great expectations can be used as a practical guidance tool to bring useful inspiration to relevant researchers.