DDI-GCN: Drug-drug interaction prediction via explainable graph convolutional networks-Reference-Cited by-同舟云学术

DDI-GCN: Drug-drug interaction prediction via explainable graph convolutional networks

Published:2023-10 Issue: Volume:144 Page:102640
ISSN:0933-3657
Container-title:Artificial Intelligence in Medicine
language:en
Short-container-title:Artificial Intelligence in Medicine

Author:

Zhong Yi,Zheng Houbing,Chen Xiaoming,Zhao Yu,Gao Tingfang,Dong Huiqun,Luo Heng,Weng Zuquan

Publisher

Elsevier BV

Subject

Artificial Intelligence,Medicine (miscellaneous)

Reference62 articles.

1. A population-based analysis of the risk of drug interaction between clarithromycin and statins for hospitalisation or death;Mesgarpour;Lipids Health Dis,2015

2. Methadone withdrawal when starting an antiretroviral regimen including nevirapine;Heelon;Pharmacotherapy,1999

3. Drug-drug interactions associated with length of stay and cost of hospitalization;Moura;J Pharm Pharm Sci,2009

4. Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data;Zhang;BMC Bioinf,2017

5. Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge;Takeda;J Chem,2017

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