School of Information Science and Engineering, Qufu Normal University, Rizhao, China.
Library of Qufu Normal University, Qufu Normal University, Rizhao, China.
Mol Omics. 2019 Apr 1;15(2):130-137. doi: 10.1039/c8mo00244d. Epub 2019 Feb 6.
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-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.
随着生物研究和科学实验的发展，已经发现 microRNAs（miRNAs）与许多严重的人类疾病密切相关；然而，发现正确的 miRNA-疾病关联既耗时又具有挑战性。因此，开发一些新方法是非常必要的。尽管现有的方法在这方面非常有帮助，但它们都存在一些缺点；因此，需要开发一些新的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-范数来增加稀疏性。此外，还引入了高斯相互作用轮廓核。实验表明，我们的方法是可行的，并且具有较高的 AUC 值。此外，还使用五重交叉验证方法来评估该方法。在数据集上进行了模拟实验来预测一些新的关联，得到的实验结果令人满意，这证明了我们的方法确实是可行的。
