Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer percep...
Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach
About this item
Full title
Author / Creator
Chen, Y-H , Shih, Y-T , Chien, C-S and Tsai, C-S
Publisher
United States: Public Library of Science
Journal title
Language
English
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Publication information
Publisher
United States: Public Library of Science
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Scope and Contents
Contents
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 the datasets of known drugs and side effect network...
Alternative Titles
Full title
Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach
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Author / Creator
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Record Identifier
TN_cdi_plos_journals_2754486254
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2754486254
Other Identifiers
ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0266435
