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MVGCN: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite networks
Bioinformatics ( IF 5.8 ) Pub Date : 2021-09-08 , DOI: 10.1093/bioinformatics/btab651
Haitao Fu 1 , Feng Huang 1 , Xuan Liu 1 , Yang Qiu 1 , Wen Zhang 1
Affiliation  

Motivation There are various interaction/association bipartite networks in biomolecular systems. Identifying unobserved links in biomedical bipartite networks helps to understand the underlying molecular mechanisms of human complex diseases and thus benefits the diagnosis and treatment of diseases. Although a great number of computational methods have been proposed to predict links in biomedical bipartite networks, most of them heavily depend on features and structures involving the bioentities in one specific bipartite network, which limits the generalization capacity of applying the models to other bipartite networks. Meanwhile, bioentities usually have multiple features, and how to leverage them has also been challenging. Results In this study, we propose a novel multi-view graph convolution network (MVGCN) framework for link prediction in biomedical bipartite networks. We first construct a multi-view heterogeneous network (MVHN) by combining the similarity networks with the biomedical bipartite network, and then perform a self-supervised learning strategy on the bipartite network to obtain node attributes as initial embeddings. Further, a neighborhood information aggregation (NIA) layer is designed for iteratively updating the embeddings of nodes by aggregating information from inter- and intra-domain neighbors in every view of the MVHN. Next, we combine embeddings of multiple NIA layers in each view, and integrate multiple views to obtain the final node embeddings, which are then fed into a discriminator to predict the existence of links. Extensive experiments show MVGCN performs better than or on par with baseline methods and has the generalization capacity on six benchmark datasets involving three typical tasks. Availability and implementation Source code and data can be downloaded from https://github.com/fuhaitao95/MVGCN. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译:

MVGCN:通过多视图图卷积网络进行数据集成,用于预测生物医学二分网络中的链接

动机 生物分子系统中存在各种相互作用/缔合二分网络。识别生物医学二分网络中未观察到的联系有助于理解人类复杂疾病的潜在分子机制,从而有利于疾病的诊断和治疗。尽管已经提出了大量计算方法来预测生物医学二分网络中的链接,但它们中的大多数严重依赖于涉及特定二分网络中生物实体的特征和结构,这限制了将模型应用于其他二分网络的泛化能力。同时,生物实体通常具有多种特征,如何利用它们也一直具有挑战性。结果在这项研究中,我们提出了一种用于生物医学二分网络链接预测的新型多视图图卷积网络 (MVGCN) 框架。我们首先通过将相似性网络与生物医学二分网络相结合来构建多视图异构网络 (MVHN),然后在二分网络上执行自监督学习策略以获得节点属性作为初始嵌入。此外,邻域信息聚合 (NIA) 层被设计用于通过在 MVHN 的每个视图中聚合来自域间和域内邻居的信息来迭代更新节点的嵌入。接下来,我们在每个视图中组合多个 NIA 层的嵌入,并整合多个视图以获得最终的节点嵌入,然后将其输入鉴别器以预测链接的存在。大量实验表明 MVGCN 的性能优于或与基线方法相当,并且在涉及三个典型任务的六个基准数据集上具有泛化能力。可用性和实现 源代码和数据可以从 https://github.com/fuhaitao95/MVGCN 下载。补充信息 补充数据可在 Bioinformatics 在线获取。
更新日期:2021-09-08
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