With the development of biological research and scientific experiments, it has been discovered that microRNAs (miRNAs) are closely related to many serious human diseases; however, finding the correct miRNA–disease associations is both time consuming and challenging. Therefore, it is very necessary to develop some new methods. Although the existing methods are very helpful in this regard, they all present some shortcomings; thus, some new methods need to be developed to overcome these shortcomings. In this study, a method based on dual network sparse graph regularized matrix factorization (DNSGRMF) was proposed, which increased the sparsity by adding the L_2,1-norm. Moreover, Gaussian interaction profile kernels were introduced. The experiments showed that our method was feasible and had a high AUC value. Additionally, the five-fold cross-validation method was used to evaluate this method. A simulation experiment was used to predict some new associations on the datasets, and the obtained experimental results were satisfactory, which proved that our method was indeed feasible.

中文翻译：

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双网络稀疏图正则化矩阵分解可预测miRNA与疾病的关联

随着生物学研究和科学实验的发展，已经发现microRNA（miRNA）与许多严重的人类疾病密切相关。但是，找到正确的miRNA-疾病关联既耗时又具有挑战性。因此，非常有必要开发一些新方法。尽管现有方法在这方面非常有帮助，但它们都存在一些缺点。因此，需要开发一些新方法来克服这些缺点。在这项研究中，提出了一种基于双网络稀疏图正则化矩阵分解（DNSGRMF）的方法，该方法通过添加L _2,1来增加稀疏性。-规范。此外，还介绍了高斯交互轮廓内核。实验表明，该方法是可行的，具有较高的AUC值。此外，五重交叉验证方法用于评估此方法。通过仿真实验预测了数据集上的一些新的关联，获得的实验结果令人满意，证明了我们的方法是切实可行的。