Rao Yidong, Xie Minzhu, Wang Hao
College of Information Science and Engineering, Hunan Normal University, Changsha, China.
Front Genet. 2022 Aug 22;13:978975. doi: 10.3389/fgene.2022.978975. eCollection 2022.
Increasing evidences show that the abnormal microRNA (miRNA) expression is related to a variety of complex human diseases. However, the current biological experiments to determine miRNA-disease associations are time consuming and expensive. Therefore, computational models to predict potential miRNA-disease associations are in urgent need. Though many miRNA-disease association prediction methods have been proposed, there is still a room to improve the prediction accuracy. In this paper, we propose a matrix completion model with bounded nuclear norm regularization to predict potential miRNA-disease associations, which is called BNNRMDA. BNNRMDA at first constructs a heterogeneous miRNA-disease network integrating the information of miRNA self-similarity, disease self-similarity, and the known miRNA-disease associations, which is represented by an adjacent matrix. Then, it models the miRNA-disease prediction as a relaxed matrix completion with error tolerance, value boundary and nuclear norm minimization. Finally it implements the alternating direction method to solve the matrix completion problem. BNNRMDA makes full use of available information of miRNAs and diseases, and can deals with the data containing noise. Compared with four state-of-the-art methods, the experimental results show BNNRMDA achieved the best performance in five-fold cross-validation and leave-one-out cross-validation. The case studies on two complex human diseases showed that 47 of the top 50 prediction results of BNNRMDA have been verified in the latest HMDD database.
越来越多的证据表明，微小RNA（miRNA）表达异常与多种复杂的人类疾病相关。然而，目前用于确定miRNA与疾病关联的生物学实验既耗时又昂贵。因此，迫切需要能够预测潜在miRNA与疾病关联的计算模型。尽管已经提出了许多miRNA与疾病关联的预测方法，但在预测准确性方面仍有提升空间。在本文中，我们提出了一种带有有界核范数正则化的矩阵补全模型来预测潜在的miRNA与疾病关联，称为BNNRMDA。BNNRMDA首先构建一个整合了miRNA自相似性、疾病自相似性以及已知miRNA与疾病关联信息的异质miRNA与疾病网络，该网络由一个邻接矩阵表示。然后，它将miRNA与疾病的预测建模为一个具有容错、值边界和核范数最小化的松弛矩阵补全问题。最后，它采用交替方向法来解决矩阵补全问题。BNNRMDA充分利用了miRNA和疾病的可用信息，并且能够处理包含噪声的数据。与四种最先进的方法相比，实验结果表明BNNRMDA在五折交叉验证和留一法交叉验证中表现最佳。对两种复杂人类疾病的案例研究表明，BNNRMDA前50个预测结果中有47个已在最新的HMDD数据库中得到验证。
