Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach

We apply a heterogeneous graph convolution network (GCN) combined with a multi-layer perceptron (MLP) denoted by GCNMLP to explore the potential side effects of drugs. Here the SIDER, OFFSIDERS, and FAERS are used as the datasets. We integrate the drug information with similar characteristics from t...

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Detalles Bibliográficos
Autores principales: Chen, Y.-H., Shih, Y.-T., Chien, C.-S., Tsai, C.-S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9750037/
https://www.ncbi.nlm.nih.gov/pubmed/36516131
http://dx.doi.org/10.1371/journal.pone.0266435

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9750037/
https://www.ncbi.nlm.nih.gov/pubmed/36516131
http://dx.doi.org/10.1371/journal.pone.0266435

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