Predicting Biomedical Interactions With Higher-Order Graph Convolutional Networks.
Predicting Biomedical Interactions With Higher-Order Graph Convolutional Networks.
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DOI：
10.1109/tcbb.2021.3059415
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发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Haake AR
中科院分区：
文献类型：
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作者：
Kc K;Li R;Cui F;Haake AR
生物医学相互作用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假定的药物靶点方面具有不可思议的潜力。近年来，图神经网络被提出用于生物医学实体表征的有效学习，并在生物医学相互作用预测方面取得了较好的效果。这些方法只考虑来自近邻的信息，而不能从不同距离的近邻中学习到一般的混合特征。本文提出了一种高阶图卷积网络（HOGCN），用于生物医学相互作用预测。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表示。在蛋白-蛋白、药物-药物、药物-靶点和基因-疾病相互作用四个相互作用网络上的实验表明，HOGCN实现了更准确和校准的预测。当考虑到不同距离上邻居的特征表示时，HOGCN在有噪声的稀疏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相互作用预测。
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.