School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
School of Mathematical Science, Heilongjiang University, Harbin 150080, China.
Molecules. 2019 Aug 26;24(17):3099. doi: 10.3390/molecules24173099.
Identifying disease-associated microRNAs (disease miRNAs) contributes to the understanding of disease pathogenesis. Most previous computational biology studies focused on multiple kinds of connecting edges of miRNAs and diseases, including miRNA-miRNA similarities, disease-disease similarities, and miRNA-disease associations. Few methods exploited the node attribute information related to miRNA family and cluster. The previous methods do not completely consider the sparsity of node attributes. Additionally, it is challenging to deeply integrate the node attributes of miRNAs and the similarities and associations related to miRNAs and diseases. In the present study, we propose a novel method, known as MDAPred, based on nonnegative matrix factorization to predict candidate disease miRNAs. MDAPred integrates the node attributes of miRNAs and the related similarities and associations of miRNAs and diseases. Since a miRNA is typically subordinate to a family or a cluster, the node attributes of miRNAs are sparse. Similarly, the data for miRNA and disease similarities are sparse. Projecting the miRNA and disease similarities and miRNA node attributes into a common low-dimensional space contributes to estimating miRNA-disease associations. Simultaneously, the possibility that a miRNA is associated with a disease depends on the miRNA's neighbour information. Therefore, MDAPred deeply integrates projections of multiple kinds of connecting edges, projections of miRNAs node attributes, and neighbour information of miRNAs. The cross-validation results showed that MDAPred achieved superior performance compared to other state-of-the-art methods for predicting disease-miRNA associations. MDAPred can also retrieve more actual miRNA-disease associations at the top of prediction results, which is very important for biologists. Additionally, case studies of breast, lung, and pancreatic cancers further confirmed the ability of MDAPred to discover potential miRNA-disease associations.
鉴定与疾病相关的 microRNA（disease miRNAs）有助于理解疾病的发病机制。大多数以前的计算生物学研究都集中在 miRNA 和疾病的多种连接边缘上，包括 miRNA-miRNA 相似性、疾病-疾病相似性和 miRNA-疾病关联。很少有方法利用与 miRNA 家族和聚类相关的节点属性信息。以前的方法没有完全考虑节点属性的稀疏性。此外，深入整合 miRNA 的节点属性以及与 miRNA 和疾病相关的相似性和关联具有挑战性。在本研究中，我们提出了一种新的方法，称为 MDAPred，它基于非负矩阵分解来预测候选疾病 miRNAs。MDAPred 整合了 miRNA 的节点属性以及与 miRNA 和疾病相关的相似性和关联。由于 miRNA 通常隶属于一个家族或一个聚类，因此 miRNA 的节点属性是稀疏的。同样，miRNA 和疾病相似性的数据也是稀疏的。将 miRNA 和疾病相似性以及 miRNA 节点属性投影到一个共同的低维空间有助于估计 miRNA-疾病关联。同时，一个 miRNA 与疾病相关的可能性取决于 miRNA 的邻居信息。因此，MDAPred 深度整合了多种连接边缘的投影、miRNA 节点属性的投影以及 miRNA 的邻居信息。交叉验证结果表明，MDAPred 在预测疾病-miRNA 关联方面的性能优于其他最先进的方法。MDAPred 还可以在预测结果的顶部检索到更多实际的 miRNA-疾病关联，这对生物学家来说非常重要。此外，乳腺癌、肺癌和胰腺癌的案例研究进一步证实了 MDAPred 发现潜在 miRNA-疾病关联的能力。
