DSGAT: predicting frequencies of drug side effects by graph attention networks
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Title
DSGAT: predicting frequencies of drug side effects by graph attention networks
Authors
Keywords
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Journal
BRIEFINGS IN BIOINFORMATICS
Volume -, Issue -, Pages -
Publisher
Oxford University Press (OUP)
Online
2021-12-21
DOI
10.1093/bib/bbab586
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- (2020) Miranda Davies et al. DRUG SAFETY
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- (2020) Vicki Osborne et al. DRUG SAFETY
- Comparative Toxicogenomics Database (CTD): update 2021
- (2020) Allan Peter Davis et al. NUCLEIC ACIDS RESEARCH
- Predicting the frequencies of drug side effects
- (2020) Diego Galeano et al. Nature Communications
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- (2020) Wei Zhao et al. CANCER CELL
- Predicting drug side effects with compact integration of heterogeneous networks
- (2019) Xian Zhao et al. Current Bioinformatics
- Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects
- (2019) Phuong A. Nguyen et al. Nature Communications
- Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism
- (2019) Zhaoping Xiong et al. JOURNAL OF MEDICINAL CHEMISTRY
- Graph Convolutional Neural Networks for Predicting Drug-Target Interactions
- (2019) Wen Torng et al. Journal of Chemical Information and Modeling
- Frequency and type of drug-related side effects necessitating treatment discontinuation in the Swiss Inflammatory Bowel Disease Cohort
- (2018) Sébastien Godat et al. EUROPEAN JOURNAL OF GASTROENTEROLOGY & HEPATOLOGY
- Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition
- (2018) Sabrina Jaeger et al. Journal of Chemical Information and Modeling
- Identification of drug-side effect association via multiple information integration with centered kernel alignment
- (2018) Yijie Ding et al. NEUROCOMPUTING
- Identification of Drug-Side Effect Association via Semisupervised Model and Multiple Kernel Learning
- (2018) Yijie Ding et al. IEEE Journal of Biomedical and Health Informatics
- DrugBank 5.0: a major update to the DrugBank database for 2018
- (2017) David S Wishart et al. NUCLEIC ACIDS RESEARCH
- Drug-induced adverse events prediction with the LINCS L1000 data
- (2016) Zichen Wang et al. BIOINFORMATICS
- A curated and standardized adverse drug event resource to accelerate drug safety research
- (2016) Juan M. Banda et al. Scientific Data
- The SIDER database of drugs and side effects
- (2015) Michael Kuhn et al. NUCLEIC ACIDS RESEARCH
- Data-Driven Prediction of Drug Effects and Interactions
- (2012) N. P. Tatonetti et al. Science Translational Medicine
- Predicting adverse side effects of drugs
- (2011) Liang-Chin Huang et al. BMC GENOMICS
- An Algorithmic Framework for Predicting Side Effects of Drugs
- (2011) Nir Atias et al. JOURNAL OF COMPUTATIONAL BIOLOGY
- Predicting Adverse Drug Events Using Pharmacological Network Models
- (2011) A. Cami et al. Science Translational Medicine
- Principles of early drug discovery
- (2010) JP Hughes et al. BRITISH JOURNAL OF PHARMACOLOGY
- PubChem: a public information system for analyzing bioactivities of small molecules
- (2009) Y. Wang et al. NUCLEIC ACIDS RESEARCH
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