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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Veröffentlicht in:	BMC bioinformatics Jg. 21; H. Suppl 3; S. 94 - 13
Hauptverfasser:	Harada, Shonosuke, Akita, Hirotaka, Tsubaki, Masashi, Baba, Yukino, Takigawa, Ichigaku, Yamanishi, Yoshihiro, Kashima, Hisashi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 23.04.2020 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	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-Zugang:	Volltext
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