Key Laboratory of High Confidence Software Technologies of Ministry of Education, School of Computer Science, Peking University, Beijing, China.
International Computer Science Institute and Department of Statistics, University of California, Berkeley, Berkeley CA, USA.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac340.
Increasing evidence has suggested that microRNAs (miRNAs) are important biomarkers of various diseases. Numerous graph neural network (GNN) models have been proposed for predicting miRNA-disease associations. However, the existing GNN-based methods have over-smoothing issue-the learned feature embeddings of miRNA nodes and disease nodes are indistinguishable when stacking multiple GNN layers. This issue makes the performance of the methods sensitive to the number of layers, and significantly hurts the performance when more layers are employed. In this study, we resolve this issue by a novel self-feature-based graph autoencoder model, shortened as SFGAE. The key novelty of SFGAE is to construct miRNA-self embeddings and disease-self embeddings, and let them be independent of graph interactions between two types of nodes. The novel self-feature embeddings enrich the information of typical aggregated feature embeddings, which aggregate the information from direct neighbors and hence heavily rely on graph interactions. SFGAE adopts a graph encoder with attention mechanism to concatenate aggregated feature embeddings and self-feature embeddings, and adopts a bilinear decoder to predict links. Our experiments show that SFGAE achieves state-of-the-art performance. In particular, SFGAE improves the average AUC upon recent GAEMDA [1] on the benchmark datasets HMDD v2.0 and HMDD v3.2, and consistently performs better when less (e.g. 10%) training samples are used. Furthermore, SFGAE effectively overcomes the over-smoothing issue and performs stably well on deeper models (e.g. eight layers). Finally, we carry out case studies on three human diseases, colon neoplasms, esophageal neoplasms and kidney neoplasms, and perform a survival analysis using kidney neoplasm as an example. The results suggest that SFGAE is a reliable tool for predicting potential miRNA-disease associations.
越来越多的证据表明，microRNAs（miRNAs）是各种疾病的重要生物标志物。已经提出了许多图神经网络（GNN）模型来预测 miRNA-疾病关联。然而，现有的基于 GNN 的方法存在过度平滑问题-当堆叠多个 GNN 层时，miRNA 节点和疾病节点的学习特征嵌入难以区分。这个问题使得方法的性能对层数敏感，并且当使用更多层时会显著降低性能。在这项研究中，我们通过一种新的基于自特征的图自动编码器模型（简称 SFGAE）解决了这个问题。SFGAE 的关键新颖之处在于构建 miRNA 自嵌入和疾病自嵌入，并使它们独立于两种类型节点之间的图交互。新颖的自特征嵌入丰富了典型聚合特征嵌入的信息，这些嵌入聚合了来自直接邻居的信息，因此严重依赖于图交互。SFGAE 采用具有注意力机制的图编码器来串联聚合特征嵌入和自特征嵌入，并采用双线性解码器来预测链接。我们的实验表明，SFGAE 达到了最先进的性能。特别是，SFGAE 在基准数据集 HMDD v2.0 和 HMDD v3.2 上提高了最近的 GAEMDA[1]的平均 AUC，并且在使用较少（例如 10%）训练样本时表现更好。此外，SFGAE 有效地克服了过度平滑问题，并在更深的模型（例如 8 层）上表现稳定。最后，我们对三种人类疾病（结肠癌、食管癌和肾癌）进行了案例研究，并以肾癌为例进行了生存分析。结果表明，SFGAE 是预测潜在 miRNA-疾病关联的可靠工具。
