microRNAs（miRNAs）在生命进程中发挥着重要作用.近年来，预测miRNAs与疾病的关联关系成为一个研究热点.当前，计算方法整体上可以分为两大类：基于相似度度量的方法和基于机器学习的方法.前者通过度量网络中节点之间的关联强度预测miRNA-疾病关联，但需要构建高质量的生物网络模型；后者将机器学习相关算法应用到这个问题中，但需要构建高可信度的负例集合.基于以上困难和不足，提出了一种计算模型BNPDCMDA，用于预测miRNAs-疾病关联关系.该方法首先构建miRNA-疾病双层网络模型，然后利用miRNA的功能相似度对其进行基于密度的聚类，进而将二分网络投影应用于聚类后的miRNAs及疾病集合构成的miRNA-疾病双层子网中，最终完成对miRNA与疾病关联关系的预测.实验结果表明，采用留一交叉验证法得到的AUC值可达99.08%，明显优于当前其他高效方法.最后，采用BNPDCMDA方法对某些常见疾病所关联的miRNAs进行预测，实验结果获得了文献的支持，进一步表明了该方法的有效性.

MicroRNAs (miRNAs) play an important role in the process of life. In recent years, predicting the associations between miRNAs and diseases has become a hot topic in research. Existing computational methods can be mainly divided into two categories:methods based on similarity measurement, and methods based on machine learning. The former approaches predict miRNA-disease associations by measuring similarity of nodes in the biological networks, but they need to build high quality biological networks. The latter approaches apply machine learning algorithms to this problem, but they need to build a negative collection of high credibility. To address those shortcomings, this paper presents a novel computational model called BNPDCMDA (bipartite network projection based on density clustering to predict miRNA-disease associations) to predict miRNAs-disease associations. First, a miRNA-disease double-layer network model is constructed. Then, similarity of miRNAs is used to perform density clustering. Next, bipartite network projection is applied to miRNA-disease double-layer composed of density clustered miRNAs and disease sets. Finally, predictions for miRNA-disease association are performed. Further experimental results show that the proposed approach achieves AUC of 99.08% by using the leave-one-out cross-validation test, which demonstrates better predictive performance of BNPDCMDA than other methods. Moreover, certain miRNAs associated common diseases are predicted by BNPDCMDA.

国家自然科学基金（61571163，61532014，61671189，61402132）；国家重点基础研究发展计划（973）（2016YFC0901902）