IEEE/ACM Trans Comput Biol Bioinform. 2022 Mar-Apr;19(2):676-687. doi: 10.1109/TCBB.2021.3059415. Epub 2022 Apr 1.
Biomedical interaction networks have incredible potential to be useful in the prediction of biologically meaningful interactions, identification of network biomarkers of disease, and the discovery of putative drug targets. Recently, graph neural networks have been proposed to effectively learn representations for biomedical entities and achieved state-of-the-art results in biomedical interaction prediction. These methods only consider information from immediate neighbors but cannot learn a general mixing of features from neighbors at various distances. In this paper, we present a higher-order graph convolutional network (HOGCN)to aggregate information from the higher-order neighborhood for biomedical interaction prediction. Specifically, HOGCN collects feature representations of neighbors at various distances and learns their linear mixing to obtain informative representations of biomedical entities. Experiments on four interaction networks, including protein-protein, drug-drug, drug-target, and gene-disease interactions, show that HOGCN achieves more accurate and calibrated predictions. HOGCN performs well on noisy, sparse interaction networks when feature representations of neighbors at various distances are considered. Moreover, a set of novel interaction predictions are validated by literature-based case studies.
生物医学交互网络在预测有生物学意义的相互作用、识别疾病的网络生物标志物和发现潜在药物靶点方面具有巨大的潜力。最近，图神经网络被提出用于有效地学习生物医学实体的表示，并在生物医学交互预测中取得了最先进的结果。这些方法仅考虑来自直接邻居的信息，但不能从各种距离的邻居学习到一般的特征混合。在本文中，我们提出了一种高阶图卷积网络（HOGCN），用于生物医学交互预测中从高阶邻域聚合信息。具体来说，HOGCN 收集不同距离邻居的特征表示，并学习它们的线性混合，以获得生物医学实体的信息丰富表示。在包括蛋白质-蛋白质、药物-药物、药物-靶标和基因-疾病相互作用在内的四个交互网络上的实验表明，HOGCN 实现了更准确和校准的预测。当考虑不同距离邻居的特征表示时，HOGCN 在嘈杂、稀疏的交互网络上表现良好。此外，通过基于文献的案例研究验证了一组新的交互预测。
