Identifying the cause-and-effect mechanism behind the drug–disease associations is a challenging task. Recent studies indicate that microRNAs (miRNAs) play critical roles in human diseases. Targeting specific miRNAs with drugs to treat diseases provides a new aspect for drug repositioning. Drug repositioning provides a way to identify new clinical applications for approved drugs. Drug discovery is expensive and complicated. Therefore, computational methods are necessary for predicting the potential associations between drugs and diseases based on the target miRNAs. Our approach bilateral-inductive matrix completion (BIMC) performed two rounds of inductive matrix completion algorithm, one on the drug–miRNA and another on the miRNA–disease, association matrices, and integrated the results for predicting the drug–disease relationships through the target miRNAs. The fundamental idea of inductive matrix completion (IMC) is to fill the unknown entries of the association matrices by utilizing existing associations and side information. In our study, the integrated similarities of drugs, miRNAs, and diseases were utilized as side information. Our method predicts drug–miRNA and miRNA–disease associations, as intermediate results. To estimate the performance of our approach, we conducted leave-one-out cross-validation (LOOCV) experiments. The method could achieve AUC scores of 0.792, 0.759, and 0.791 in drug–disease, drug–miRNA, and miRNA–diseases association predictions. The results and case studies indicate the prediction ability of our method, and it is superior to previous models with high robustness. The proposed approach predicts new drug–disease relationships and the causal miRNAs. The top predicted relationships are the promising candidates, and they are released for further biological tests.

查明毒品-疾病关联背后的因果机制是一项艰巨的任务。最近的研究表明，microRNA（miRNA）在人类疾病中起关键作用。用药物靶向特定的miRNA来治疗疾病为药物重新定位提供了新的方面。药物重新定位提供了一种方法来识别已批准药物的新临床应用。药物发现既昂贵又复杂。因此，计算方法对于基于靶标miRNA预测药物与疾病之间的潜在关联是必要的。我们的方法双边感应矩阵完成（BIMC）执行了两轮感应矩阵完成算法，一轮针对药物– miRNA，另一轮针对miRNA –疾病，关联矩阵，并整合了通过目标miRNA预测药物-疾病关系的结果。归纳矩阵完成（IMC）的基本思想是通过利用现有的关联和辅助信息来填充关联矩阵的未知条目。在我们的研究中，药物，miRNA和疾病的综合相似性被用作辅助信息。我们的方法预测药物– miRNA和miRNA –疾病的关联，作为中间结果。为了评估我们方法的性能，我们进行了留一法交叉验证（LOOCV）实验。在药物疾病，药物-miRNA和miRNA-疾病关联预测中，该方法可达到0.792、0.759和0.791的AUC评分。结果和案例研究表明了我们方法的预测能力，并且比以前的模型具有更高的鲁棒性。拟议的方法可以预测新的药物-疾病关系和因果miRNA。预测的最高关联是有希望的候选者，它们被释放以进行进一步的生物学测试。