Predicting the associations of miRNAs and diseases may uncover the causation of various diseases. Many methods are emerging to tackle the sparse and unbalanced disease related miRNA prediction. Here, we propose a Probabilistic matrix decomposition combined with neighbor learning to identify MiRNA-Disease Associations utilizing heterogeneous data(PMDA). First, we build similarity networks for diseases and miRNAs, respectively, by integrating semantic information and functional interactions. Second, we construct a neighbor learning model in which the neighbor information of individual miRNA or disease is utilized to enhance the association relationship to tackle the spare problem. Third, we predict the potential association between miRNAs and diseases via probability matrix decomposition. The experimental results show that PMDA is superior to other five methods in sparse and unbalanced data. The case study shows that the new miRNA-disease interactions predicted by the PMDA are effective and the performance of the PMDA is superior to other methods.

中文翻译：

---

通过将概率矩阵特征分解与邻居学习相结合来预测 miRNA 与疾病的关联

预测 miRNA 与疾病的关联可能会揭示各种疾病的病因。出现了许多方法来解决与疾病相关的 miRNA 预测的稀疏和不平衡问题。在这里，我们提出了一种概率矩阵分解与邻域学习相结合的方法，以利用异构数据 (PMDA) 识别 MiRNA-疾病关联。首先，我们通过整合语义信息和功能交互，分别为疾病和 miRNA 建立相似性网络。其次，我们构建了一个邻居学习模型，其中利用个体 miRNA 或疾病的邻居信息来增强关联关系以解决备用问题。第三，我们通过概率矩阵分解预测 miRNA 与疾病之间的潜在关联。实验结果表明，PMDA在稀疏和不平衡数据方面优于其他五种方法。案例研究表明，PMDA 预测的新的 miRNA-疾病相互作用是有效的，并且 PMDA 的性能优于其他方法。