Ji Cunmei, Wang Yutian, Ni Jiancheng, Zheng Chunhou, Su Yansen
School of Cyber Science and Engineering, Qufu Normal University, Qufu, China.
School of Artificial Intelligence, Anhui University, Hefei, China.
Front Genet. 2021 Aug 25;12:727744. doi: 10.3389/fgene.2021.727744. eCollection 2021.
In recent years, more and more evidence has shown that microRNAs (miRNAs) play an important role in the regulation of post-transcriptional gene expression, and are closely related to human diseases. Many studies have also revealed that miRNAs can be served as promising biomarkers for the potential diagnosis and treatment of human diseases. The interactions between miRNA and human disease have rarely been demonstrated, and the underlying mechanism of miRNA is not clear. Therefore, computational approaches has attracted the attention of researchers, which can not only save time and money, but also improve the efficiency and accuracy of biological experiments. In this work, we proposed a Heterogeneous Graph Attention Networks (GAT) based method for miRNA-disease associations prediction, named HGATMDA. We constructed a heterogeneous graph for miRNAs and diseases, introduced weighted DeepWalk and GAT methods to extract features of miRNAs and diseases from the graph. Moreover, a fully-connected neural networks is used to predict correlation scores between miRNA-disease pairs. Experimental results under five-fold cross validation (five-fold CV) showed that HGATMDA achieved better prediction performance than other state-of-the-art methods. In addition, we performed three case studies on breast neoplasms, lung neoplasms and kidney neoplasms. The results showed that for the three diseases mentioned above, 50 out of top 50 candidates were confirmed by the validation datasets. Therefore, HGATMDA is suitable as an effective tool to identity potential diseases-related miRNAs.
近年来，越来越多的证据表明，微小RNA（miRNA）在转录后基因表达调控中发挥着重要作用，并且与人类疾病密切相关。许多研究还表明，miRNA可作为人类疾病潜在诊断和治疗的有前景的生物标志物。miRNA与人类疾病之间的相互作用鲜有证实，其潜在机制尚不清楚。因此，计算方法引起了研究人员的关注，它不仅可以节省时间和金钱，还能提高生物学实验的效率和准确性。在这项工作中，我们提出了一种基于异构图注意力网络（GAT）的miRNA-疾病关联预测方法，名为HGATMDA。我们构建了一个miRNA和疾病的异构图，引入加权深度游走和GAT方法从图中提取miRNA和疾病的特征。此外，使用全连接神经网络预测miRNA-疾病对之间的相关分数。五折交叉验证（五折CV）下的实验结果表明，HGATMDA比其他现有方法具有更好的预测性能。此外，我们对乳腺肿瘤、肺肿瘤和肾肿瘤进行了三个案例研究。结果表明，对于上述三种疾病，前50个候选者中有50个被验证数据集证实。因此，HGATMDA适合作为识别潜在疾病相关miRNA的有效工具。
