Table1_Prediction of potential small molecule−miRNA associations based on heterogeneous network representation learning.DOCX

MicroRNAs (miRNAs) are closely associated with the occurrences and developments of many complex human diseases. Increasing studies have shown that miRNAs emerge as new therapeutic targets of small molecule (SM) drugs. Since traditional experiment methods are expensive and time consuming, it is particularly crucial to find efficient computational approaches to predict potential small molecule-miRNA (SM-miRNA) associations. Considering that integrating multi-source heterogeneous information related with SM-miRNA association prediction would provide a comprehensive insight into the features of both SMs and miRNAs, we proposed a novel model of Small Molecule-MiRNA Association prediction based on Heterogeneous Network Representation Learning (SMMA-HNRL) for more precisely predicting the potential SM-miRNA associations. In SMMA-HNRL, a novel heterogeneous information network was constructed with SM nodes, miRNA nodes and disease nodes. To access and utilize of the topological information of the heterogeneous information network, feature vectors of SM and miRNA nodes were obtained by two different heterogeneous network representation learning algorithms (HeGAN and HIN2Vec) respectively and merged with connect operation. Finally, LightGBM was chosen as the classifier of SMMA-HNRL for predicting potential SM-miRNA associations. The 10-fold cross validations were conducted to evaluate the prediction performance of SMMA-HNRL, it achieved an area under of ROC curve of 0.9875, which was superior to other three state-of-the-art models. With two independent validation datasets, the test experiment results revealed the robustness of our model. Moreover, three case studies were performed. As a result, 35, 37, and 22 miRNAs among the top 50 predicting miRNAs associated with 5-FU, cisplatin, and imatinib were validated by experimental literature works respectively, which confirmed the effectiveness of SMMA-HNRL. The source code and experimental data of SMMA-HNRL are available at https://github.com/SMMA-HNRL/SMMA-HNRL.

微小RNA（MicroRNAs, miRNAs）与诸多人类复杂疾病的发生发展紧密相关。越来越多的研究证实，miRNAs已成为小分子（small molecule, SM）药物的新型治疗靶点。由于传统实验方法存在成本高昂、耗时冗长的弊端，开发高效的计算手段以预测潜在的小分子-微小RNA（small molecule-miRNA, SM-miRNA）关联，具备极为重要的现实意义。
考虑到整合与SM-miRNA关联预测相关的多源异构信息，能够全面刻画小分子与微小RNA的特征属性，本研究提出了一种基于异质网络表示学习（Heterogeneous Network Representation Learning, HNRL）的小分子-微小RNA关联预测模型（SMMA-HNRL），以期更精准地预测潜在SM-miRNA关联。
在SMMA-HNRL模型中，我们构建了涵盖小分子节点、微小RNA节点与疾病节点的新型异质信息网络。为获取并利用该异质信息网络的拓扑结构信息，分别通过HeGAN与HIN2Vec两种不同的异质网络表示学习算法，提取小分子与微小RNA节点的特征向量，并通过连接操作完成特征融合。
最终，选用轻量梯度提升树（LightGBM）作为SMMA-HNRL的分类器，用于预测潜在的SM-miRNA关联。我们采用十折交叉验证对SMMA-HNRL的预测性能进行评估，其ROC曲线下面积达到0.9875，优于其他3种当前领先的同类模型。
借助两个独立验证数据集开展测试实验，结果证实了本模型的鲁棒性。此外，本研究还完成了3项案例分析：针对5-氟尿嘧啶（5-FU）、顺铂（cisplatin）与伊马替尼（imatinib）三种药物，在其预测排名前50的相关微小RNA中，分别有35、37和22个得到了已发表实验文献的验证，充分验证了SMMA-HNRL的有效性。
SMMA-HNRL的源代码与实验数据可于https://github.com/SMMA-HNRL/SMMA-HNRL获取。