近年来,已发现miRNA变异和失调与人类肿瘤密切相关,识别miRNA与疾病的关联有助于理解疾病或肿瘤的发展机制,对预后具有重要意义。诊断,和治疗人类疾病。本文提出了一种基于共邻的二分异构网络链路预测方法来预测miRNA-疾病关联(BHCN)。根据二分网络的结构特点,提出了二分网络共邻居的概念,和共同邻居用于表示疾病和miRNA之间关联的概率。基于共同邻居表达的关联概率来预测孤立的疾病和新的miRNA,我们利用疾病节点之间的相似性和异构网络中miRNA节点之间的相似性来表示疾病与miRNA之间的关联概率。通过对不同数据集的留一交叉验证(LOOCV)评估模型的预测性能。BHCN在黄金基准数据集上的AUC值为0.7973,在预测数据集上得到的AUC为0.9349,优于经典全局算法。在这个案例研究中,我们对乳腺肿瘤和结肠肿瘤进行了预测性研究.前50名预测结果中的大多数都得到了三个数据库的证实,即,HMDD,miR2疾病,和dbDEMC,准确率为96%和82%。此外,BHCN可用于预测分离的疾病(没有任何已知的相关疾病)和新的miRNA(没有任何已知的相关miRNA)。在孤立疾病病例研究中,与miRNA相关的前50名乳腺肿瘤和结肠肿瘤潜能预测准确率为100%和96%,分别,从而证明BHCN对潜在相关miRNA的有利预测能力。
In recent years, miRNA variation and dysregulation have been found to be closely related to human tumors, and identifying miRNA-disease associations is helpful for understanding the mechanisms of disease or tumor development and is greatly significant for the prognosis, diagnosis, and treatment of human diseases. This article proposes a Bipartite Heterogeneous network link prediction method based on co-neighbor to predict miRNA-disease association (BHCN). According to the structural characteristics of the bipartite network, the concept of bipartite network co-neighbors is proposed, and the co-neighbors were used to represent the probability of association between disease and miRNA. To predict the isolated diseases and the new miRNA based on the association probability expressed by co-neighbors, we utilized the similarity between disease nodes and the similarity between miRNA nodes in heterogeneous networks to represent the association probability between disease and miRNA. The model\'s predictive performance was evaluated by the leave-one-out cross validation (LOOCV) on different datasets. The AUC value of BHCN on the gold benchmark dataset was 0.7973, and the AUC obtained on the prediction dataset was 0.9349, which was better than that of the classic global algorithm. In this case study, we conducted predictive studies on breast neoplasms and colon neoplasms. Most of the top 50 predicted results were confirmed by three databases, namely, HMDD, miR2disease, and dbDEMC, with accuracy rates of 96 and 82%. In addition, BHCN can be used for predicting isolated diseases (without any known associated diseases) and new miRNAs (without any known associated miRNAs). In the isolated disease case study, the top 50 of breast neoplasm and colon neoplasm potentials associated with miRNAs predicted an accuracy of 100 and 96%, respectively, thereby demonstrating the favorable predictive power of BHCN for potentially relevant miRNAs.