Dual graph convolutional neural network for predicting chemical networks
Dual graph convolutional neural network for predicting chemical networks
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DOI：
10.1186/s12859-020-3378-0
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发表时间：
2020-04-23
影响因子：
3
通讯作者：
Kashima, Hisashi
中科院分区：
文献类型：
--
作者：
Harada, Shonosuke;Akita, Hirotaka;Kashima, Hisashi
背景化合物预测是生物信息学和化学信息学的基本任务之一，因为它有助于代谢工程和药物发现的各种应用。最近可用数据量的快速增长使得统计建模和机器学习方法等计算方法的应用成为可能。一组化学相互作用和化学化合物结构都表示为图，并且包括图卷积神经网络在内的各种基于图的方法已成功应用于化学网络预测。然而，没有有效的方法，可以考虑这两种不同类型的图在端到端的方式。结果将化学网络预测问题转化为图中的链接预测问题，该图可以表示由复合图和复合间图组成的层次结构。我们提出了一种新的图卷积神经网络架构，称为双图卷积网络，它以端到端的方式从复合图和复合网络间学习复合表示。结论采用4种不同稀疏度和度分布的化学网络进行实验，结果表明，双图卷积方法在相对密集的网络中具有较高的预测性能，而在极稀疏的网络中性能较差。
Background Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the amount of available data has enabled applications of computational approaches such as statistical modeling and machine learning method. Both a set of chemical interactions and chemical compound structures are represented as graphs, and various graph-based approaches including graph convolutional neural networks have been successfully applied to chemical network prediction. However, there was no efficient method that can consider the two different types of graphs in an end-to-end manner. Results We give a new formulation of the chemical network prediction problem as a link prediction problem in a graph of graphs (GoG) which can represent the hierarchical structure consisting of compound graphs and an inter-compound graph. We propose a new graph convolutional neural network architecture called dual graph convolutional network that learns compound representations from both the compound graphs and the inter-compound network in an end-to-end manner. Conclusions Experiments using four chemical networks with different sparsity levels and degree distributions shows that our dual graph convolution approach achieves high prediction performance in relatively dense networks, while the performance becomes inferior on extremely-sparse networks.