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Abstract：
Growing evidences have indicated that microRNAs (miRNAs) play a significant role relating to many important bioprocesses; their mutations and disorders will cause the occurrence of various complex diseases. The prediction of miRNAs associated with underlying diseases via computational approaches is beneficial to identify biomarkers and discover specific medicine, which can greatly reduce the cost of diagnosis, cure, prognosis, and prevention of human diseases. However, how to further achieve a more reliable prediction of potential miRNA-disease associations with effective integration of different biological data is a challenge for researchers. In this study, we proposed a computational model by using a federated method of combined multiple-similarities fusion and space projection (MSFSP). MSFSP firstly fused the integrated disease similarity (composed of disease semantic similarity, disease functional similarity, and disease Hamming similarity) with the integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity, and miRNA Hamming similarity). Secondly, it constructed the weighted network of miRNA-disease associations from the experimentally verified Boolean network of miRNA-disease associations by using similarity networks. Finally, it calculated the prediction results by weighting miRNA space projection scores and the disease space projection scores. Leave-one-out cross-validation demonstrated that MSFSP has the distinguished predictive accuracy with area under the receiver operating characteristics curve (AUC) of 0.9613 better than that of five other existing models. In case studies, the predictive ability of MSFSP was further confirmed as 96 and 98% of the top 50 predictions for prostatic neoplasms and lung neoplasms were successfully validated by experimental evidences and supporting experimental evidences were also found for 100% of the top 50 predictions for isolated diseases.
摘要：
越来越多的证据表明,microRNA(miRNAs)在许多重要的生物过程中起着重要的作用,它们的突变和紊乱将导致各种复杂疾病的发生。通过计算方法预测与潜在疾病相关的miRNA有利于识别生物标志物和发现特定的药物。这可以大大降低诊断成本，治愈,预后,预防人类疾病。然而,如何通过有效整合不同的生物学数据来进一步实现对潜在miRNA-疾病关联的更可靠的预测是研究人员面临的挑战。在这项研究中,我们通过使用联合多相似融合和空间投影（MSFSP）的联合方法提出了一个计算模型。MSFSP首先融合了整合的疾病相似度(由疾病语义相似度,疾病功能相似性，和疾病汉明相似性）与整合的miRNA相似性（由miRNA功能相似性组成，miRNA序列相似性,和miRNA汉明相似性)。其次,它通过使用相似性网络从实验验证的miRNA-疾病关联的布尔网络构建了miRNA-疾病关联的加权网络.最后,它通过加权miRNA空间投影得分和疾病空间投影得分来计算预测结果。留一交叉验证表明，MSFSP具有出色的预测准确性，受试者工作特征曲线下面积（AUC）为0.9613，优于其他五个现有模型。在案例研究中,MSFSP的预测能力得到了进一步证实,因为前列腺肿瘤和肺肿瘤的前50个预测中的96%和98%已通过实验证据成功验证,并且对孤立疾病的前50个预测中的100%也发现了支持实验证据.
