Neural Processing Letters ( IF 3.1 ) Pub Date : 2021-02-20 , DOI: 10.1007/s11063-021-10454-5 Tian Bai , Ying Li , Ye Wang , Lan Huang
Mining biomedical entity association and extracting the implicit knowledge from biomedical entity relation networks are important for precision medicine. In this paper, we propose a novel method for implicit relation mining from biomedical multi-entity network. In the embedding part, we combine two kinds of model (1) the graph representation learning model like GraphGAN and (2) the network embedding model like VAE based SDNE, to construct a hybrid model GVS. In the prediction part, the positive samples selected from original network and the negative samples generated by ranking meta-paths are used to train kNN. To evaluate the performances of GVS, we compare the proposed method with three state-of-the-art methods (Katz, Catapult and IMC) on benchmark datasets. Moreover, we evaluate GVS on a real biomedical entity relation network, it shows advantages compared with other network embedding methods and successfully mines implicit relationships which validated by PubMed.
中文翻译:
基于混合VAE的生物医学关系挖掘网络嵌入方法
挖掘生物医学实体关联并从生物医学实体关系网络中提取隐式知识对于精确医学非常重要。在本文中,我们提出了一种从生物医学多实体网络进行隐式关系挖掘的新方法。在嵌入部分,我们结合两种模型(1)GraphGAN等图形表示学习模型和(2)基于VAE的SDNE等网络嵌入模型,构建了混合模型GVS。在预测部分,将从原始网络中选择的正样本和通过对元路径进行排名生成的负样本用于训练kNN。为了评估GVS的性能,我们在基准数据集上将提出的方法与三种最新方法(Katz,Catapult和IMC)进行了比较。此外,我们在真实的生物医学实体关系网络上评估GVS,