基于拓扑结构的度量学习与拓扑传播的miRNA-疾病关联预测算法

Topology-based metric learning and topology propagation algorithm for miRNA-disease association prediction

miRNA的突变和异常表达可能导致各种疾病,因此预测miRNA与疾病的潜在相关性对于临床医学和药物研究的发展具有重要意义。拓扑结构是miRNA-疾病预测算法的重要组成部分,然而当前算法并未有效利用拓扑结构导致预测结果并不理想。与此同时,如何有效地融合多源数据也是当前的研究趋势。针对上述问题,提出一种自适应融合异质节点结构信息算法(MMTP),通过利用节点的一阶邻居和元路径诱导网络学习结构特征,并利用度量学习和拓扑传播自适应地融合异质节点结构信息,以提升miRNA-疾病预测精度。5折交叉验证实验结果表明,MMTP在HMDD v3.2数据集上的受试者操作曲线下面积(AUC)为94.81,高于其他模型。并且在基于肾癌的案例研究中,该模型所预测的前30个miRNAs全部得到证实。上述研究证明,所提的MMTP模型可有效预测miRNA-疾病相关性。

Mutations and abnormal expressions of miRNA can potentially lead to various diseases.Hence,predicting the latent correlation between miRNA and diseases holds significant importance for the advancement of clinical medicine and drug research.The topology structure constitutes a crucial component of miRNA-disease prediction algorithms.However,the current algorithms inadequately leverage the topological structure,resulting in suboptimal predictive outcomes.Simultaneously,effectively integrating multi-source data is a current research trend.In response to the aforementioned issues,this paper proposes an adaptive algorithm for fusing heterogeneous node structure information(MMTP).MMTP enhances miRNA-disease prediction accuracy by adaptively integrating heterogeneous node structure information through the utilization of first-order neighbors and metapath-induced network learning of structural features,employing metric learning and topology propagation.Results from a 5-fold cross-validation experiment demonstrate that MMTP achieves Area Under the Curve(AUC)of receiver operating characteristic values of 94.81 on the HMDD v3.2 datasets,surpassing other models.Moreover,in a case study focused on renal cancer,all of the top 30 miRNAs predicted by the model are confirmed.The aforementioned research confirms the efficacy of the proposed MMTP model in predicting miRNA-disease correlations.