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Increasing evidence from recent research demonstrates that aberrant expressions of microRNAs (miRNAs) are linked to the development of chronic human diseases. Targeting miRNAs with bioactive small-molecules (or drugs) to regulate their activities provide an innovative insight into human disease treatment. Identifying the drugs that target particular miRNAs through the experimental study is complicated, time-consuming, and tremendously expensive. Therefore, computational researches by integrating information on drugs and miRNAs are essential for discovering potential drug-miRNA associations. Realizing the appropriate drugs that target the causal miRNAs behind diseases will contribute to miRNA mediated disease therapeutics and drug clinical applications. This study proposes an ensemble learning approach, ELDMA, that predicts novel drug-miRNA associations based on deep architecture-based classification. The method constructed features based on the integrated pairwise similarities of drugs and miRNAs and reduced the feature dimensions with principal component analysis (PCA). With the resulting features, the convolutional neural network is trained to extract intricate, high-level patterns. The deep retrieved features are given to the support vector machine classifier to infer potential drug-miRNA associations. We conducted global leave-one-out cross-validation (LOOCV), drug-fixed local LOOCV, miRNA-fixed local LOOCV, and 5fold cross-validation to evaluate the model performance. ELMDA achieved corresponding AUCs of 0.9862, 0.7426, 0.9847 and 0.9928 for Dataset 1 and AUCs of 0.8643, 0.6742, 0.8671 and 0.8521 for Dataset 2, respectively. The results and case studies illustrate the effectiveness of ELDMA in identifying novel drug-miRNA candidates. The top predicted relationships are released for future wet-lab studies.

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