Le Duc-Hau
School of Information and Communications Technology, Hanoi University of Science and Technology, Hanoi 100000, Vietnam.
Biol Methods Protoc. 2025 Sep 2;10(1):bpaf065. doi: 10.1093/biomethods/bpaf065. eCollection 2025.
MicroRNAs (miRNAs) play a critical role in disease mechanisms, making the identification of disease-associated miRNAs essential for precision medicine. We propose a novel computational method, multiplex-heterogeneous network for MiRNA-disease associations (MHMDA), which integrates multiple miRNA functional similarity networks and a disease similarity network into a multiplex-heterogeneous network. This approach employs a tailored random walk with restart algorithm to predict disease-miRNA associations, leveraging the complementary information from experimentally validated and predicted miRNA-target interactions, as well as disease phenotypic similarities. Evaluated on the human microRNA disease database and miR2Disease datasets using leave-one-out cross-validation and 5-fold cross-validation, MHMDA demonstrates superior performance, achieving area under the receiver operating characteristic curve values of 0.938 and 0.913 on human microRNA disease database and miR2Disease, respectively, and outperforming existing methods. The integration of multiplex networks enhances prediction accuracy by capturing diverse miRNA functional relationships, which directly contributes to the high area under the receiver operating characteristic curve and area under the precision-recall curve values observed. Additionally, MHMDA's stability across parameter variations and disease contexts underscores its robustness and potential for real-world applications in identifying novel disease-miRNA associations.
微小RNA（miRNA）在疾病机制中发挥着关键作用，因此识别与疾病相关的miRNA对于精准医学至关重要。我们提出了一种新颖的计算方法，即用于miRNA-疾病关联的多重异质网络（MHMDA），该方法将多个miRNA功能相似性网络和一个疾病相似性网络整合到一个多重异质网络中。这种方法采用了一种定制的带重启的随机游走算法来预测疾病与miRNA的关联，利用来自实验验证和预测的miRNA-靶标相互作用的互补信息以及疾病表型相似性。在人类微小RNA疾病数据库和miR2Disease数据集上使用留一法交叉验证和五折交叉验证进行评估时，MHMDA表现出卓越的性能，在人类微小RNA疾病数据库和miR2Disease上分别实现了受试者工作特征曲线下面积值为0.938和0.913，并且优于现有方法。多重网络的整合通过捕获不同的miRNA功能关系提高了预测准确性，这直接促成了观察到的高受试者工作特征曲线下面积和精确召回率曲线下面积值。此外，MHMDA在参数变化和疾病背景下的稳定性突出了其稳健性以及在识别新型疾病-miRNA关联方面的实际应用潜力。
