Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations

Predicting novel uses for approved drugs helps in reducing the costs of drug development and facilitates the development process. Most of previous methods focused on the multi-source data related to drugs and diseases to predict the candidate associations between drugs and diseases. There are multip...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics Jg. 25; H. 5; S. 1793 - 1804
Hauptverfasser:	Xuan, Ping, Gao, Ling, Sheng, Nan, Zhang, Tiangang, Nakaguchi, Toshiya
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Ablation Artificial neural networks attribute-level attention Chemicals Disease Diseases Drug development Drugs fully-connected autoencoder graph convolutional autoencoder Heterogeneous networks Modules multiple kinds of drug similarities Network topologies Network topology Neural networks Nodes Prediction of drug-related diseases Predictions Proteins Representations Similarity Topology
ISSN:	2168-2194, 2168-2208, 2168-2208
Online-Zugang:	Volltext
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