Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects

Background: Inferring drug-related side effects is beneficial for reducing drug development cost and time. Current computational prediction methods have concentrated on graph reasoning over heterogeneous graphs comprising the drug and side effect nodes. However, the various topologies and node attri...

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Veröffentlicht in:	Frontiers in pharmacology Jg. 14; S. 1257842
Hauptverfasser:	Xuan, Ping, Xu, Kai, Cui, Hui, Nakaguchi, Toshiya, Zhang, Tiangang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Frontiers Media S.A 04.09.2023
Schlagworte:	graph convolutional autoencoder graph generative and adversarial strategy Pharmacology representation-level attention self-calibrated pairwise attributes topologies and attributes from heterogeneous graphs
ISSN:	1663-9812, 1663-9812
Online-Zugang:	Volltext
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