Predicting microRNA-disease associations using bipartite local models and hubness-aware regression

The development and progression of numerous complex human diseases have been confirmed to be associated with microRNAs (miRNAs) by various experimental and clinical studies. Predicting potential miRNA-disease associations can help us understand the underlying molecular and cellular mechanisms of diseases and promote the development of disease treatment and diagnosis. Due to the high cost of conventional experimental verification, proposing a new computational method for miRNA-disease association prediction is an efficient and economical way. Since previous computational models ignored the hubness phenomenon, we presented a novel computational model of Bipartite Local models and Hubness-Aware Regression for MiRNA-Disease Association prediction (BLHARMDA). In this method, we first used known miRNA-disease associations to calculate the Jaccard similarity between miRNAs and between diseases, then utilized a modified kNNs model in the bipartite local model method. As a result, we effectively alleviated the detriments from ‘bad’ hubs. BLHARMDA obtained AUCs of 0.9141 and 0.8390 in the global and local leave-one-out cross validation, respectively, which outperformed most of the previous models and proved high prediction performance of BLHARMDA. Besides, the standard deviation of 0.0006 in 5-fold cross validation confirmed our model’s prediction stability and the averaged prediction accuracy of 0.9120 showed the high precision of our model. In addition, to further evaluate our model’s accuracy, we implemented BLHARMDA on three typical human diseases in three different types of case studies. As a result, 49 (Esophageal Neoplasms), 50 (Lung Neoplasms) and 50 (Carcinoma Hepatocellular) out of the top 50 related miRNAs were validated by recent experimental discoveries.

多项实验与临床研究已证实，诸多复杂人类疾病的发生发展与微小RNA（microRNAs，miRNAs）密切相关。预测潜在的miRNA-疾病关联，有助于解析疾病潜在的分子与细胞机制，推动疾病诊疗技术的发展。由于传统实验验证成本高昂，开发新型计算方法用于miRNA-疾病关联预测是一种高效且经济的可行途径。鉴于现有计算模型均未考虑枢纽性现象，本研究提出了一种全新的计算模型——基于二分局部模型与枢纽感知回归的miRNA-疾病关联预测模型（Bipartite Local models and Hubness-Aware Regression for MiRNA-Disease Association prediction，BLHARMDA）。该方法首先利用已知的miRNA-疾病关联数据，分别计算miRNA间与疾病间的雅卡尔（Jaccard）相似度，随后在二分局部模型框架中引入改进的k近邻（k-nearest neighbors, kNN）模型，从而有效缓解了“劣质”枢纽带来的负面影响。在全局留一交叉验证与局部留一交叉验证中，BLHARMDA分别取得了0.9141与0.8390的AUC值，其性能优于多数现有模型，证实了该模型优异的预测性能。此外，5折交叉验证结果显示，模型的预测标准差仅为0.0006，证实了其良好的预测稳定性；其平均预测准确率达0.9120，进一步体现了模型的高精度。为进一步验证模型的准确性，本研究针对三种典型人类疾病开展了三类不同的案例研究。结果显示，在排名前50的关联miRNA中，食管肿瘤（Esophageal Neoplasms）、肺肿瘤（Lung Neoplasms）与肝细胞癌（Carcinoma Hepatocellular）分别有49、50与50个已被近期实验研究所证实。