Lu Xinguo, Li Jinxin, Zhu Zhenghao, Yuan Yue, Chen Guanyuan, He Keren
IEEE/ACM Trans Comput Biol Bioinform. 2022 Nov-Dec;19(6):3160-3170. doi: 10.1109/TCBB.2021.3097037. Epub 2022 Dec 8.
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.
预测微小RNA（miRNA）与疾病之间的关联可能会揭示各种疾病的病因。目前出现了许多方法来解决与疾病相关的miRNA预测数据稀疏和不平衡的问题。在此，我们提出一种结合邻居学习的概率矩阵分解方法，利用异构数据识别miRNA-疾病关联（PMDA）。首先，我们通过整合语义信息和功能相互作用，分别构建疾病和miRNA的相似性网络。其次，我们构建一个邻居学习模型，利用单个miRNA或疾病的邻居信息来增强关联关系，以解决稀疏问题。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，在稀疏和不平衡数据方面，PMDA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，且PMDA的性能优于其他方法。
