Dual graph convolutional neural network for predicting chemical networks

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 computat...

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Published in:BMC bioinformatics Vol. 21; no. Suppl 3; pp. 94 - 13
Main Authors: Harada, Shonosuke, Akita, Hirotaka, Tsubaki, Masashi, Baba, Yukino, Takigawa, Ichigaku, Yamanishi, Yoshihiro, Kashima, Hisashi
Format: Journal Article
Language:English
Published: London BioMed Central 23.04.2020
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Algorithms
Analysis
Artificial neural networks
Bioinformatics
Biomedical and Life Sciences
Chemical bonds
Chemical compounds
Chemical interactions
Chemical network prediction
Computational Biology - methods
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer applications
Computer architecture
Computer Graphics
Convolution
Drug Discovery
Graph convolutional neural network
Graph of graphs
Graph representations
Graphical representations
Graphs
Learning algorithms
Life Sciences
Machine learning
Mathematical models
Metabolic engineering
Metabolism
Microarrays
Models, Chemical
Neural networks
Neural Networks, Computer
Predictions
Statistical models
Structural hierarchy
Taiwan
Chemical network prediction
Graph convolutional neural network
Graph of graphs
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary: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.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-020-3378-0