An increasing amount of evidence indicates that microRNAs (miRNAs) are closely related to many important biological processes and play a significant role in various human diseases. More and more researchers have begun to seek effective methods to predict potential miRNA–disease associations. However, reliable computational methods to predict potential disease-related miRNAs are lacking. In this study, we developed a new miRNA–disease association prediction model called Negative-Aware and rating-based Recommendation algorithm for miRNA–Disease Association prediction (NARRMDA) based on the known miRNA–disease associations in the HMDD database, miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. NARRMDA combined a rating-based recommendation algorithm and a negative-aware algorithm to score and rank miRNAs without known associations with investigated diseases. Furthermore, we used leave-one-out cross validation to evaluate the accuracy of NARRMDA and compared NARRMDA with four previous classical prediction models (RLSMDA, HDMP, RWRMDA and MCMDA). As it turned out, NARRMDA and the other four prediction models achieved AUCs of 0.8053, 0.6953, 0.7702, 0.7891 and 0.7718, respectively, which proved that NARRMDA has superior performance of prediction accuracy. Furthermore, we verified the prediction results associated with colon neoplasms, esophageal neoplasms, lymphoma and breast neoplasms by two different validation schemas. In these case studies, 92%, 84%, 92%, and 100% of the top 50 potential miRNAs for these four diseases were confirmed by experimental discoveries, respectively. These results further show that NARRMDA has reliable performance of prediction ability.

中文翻译：

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NARRMDA：miRNA-疾病关联预测的基于负感知和基于评级的推荐算法

越来越多的证据表明，microRNA（miRNA）与许多重要的生物学过程密切相关，并在各种人类疾病中发挥重要作用。越来越多的研究人员开始寻求有效的方法来预测潜在的miRNA-疾病关联。但是，缺乏可靠的计算方法来预测潜在的疾病相关miRNA。在这项研究中，我们基于HMDD数据库中已知的miRNA-疾病关联，miRNA功能相似性，疾病的语义相似度和高斯相互作用曲线的核相似度。NARRMDA结合了基于评级的推荐算法和否定感知算法，对没有已知与被调查疾病相关联的miRNA进行评分和排名。此外，我们使用留一法交叉验证来评估NARRMDA的准确性，并将NARRMDA与四个先前的经典预测模型（RLSMDA，HDMP，RWRMDA和MCMDA）进行比较。事实证明，NARRMDA和其他四个预测模型的AUC分别为0.8053、0.6953、0.7702、0.7891和0.7718，这证明NARRMDA具有优异的预测准确性。此外，我们通过两种不同的验证方案验证了与结肠肿瘤，食道肿瘤，淋巴瘤和乳腺肿瘤相关的预测结果。在这些案例研究中，分别为92％，84％，92％，实验发现分别确认了这四种疾病的前50种潜在miRNA中的100％和100％。这些结果进一步表明，NARRMDA具有可靠的预测能力。