Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model
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Title
Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model
Authors
Keywords
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Journal
JOURNAL OF MEDICAL INTERNET RESEARCH
Volume 21, Issue 2, Pages e11016
Publisher
JMIR Publications Inc.
Online
2018-10-24
DOI
10.2196/11016
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Related references
Note: Only part of the references are listed.- DrugBank 5.0: a major update to the DrugBank database for 2018
- (2017) David S Wishart et al. NUCLEIC ACIDS RESEARCH
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- (2016) J. J. Coleman et al. CLINICAL MEDICINE
- Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature
- (2016) Ayush Singhal et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
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- (2015) Ming Yang et al. JOURNAL OF BIOMEDICAL INFORMATICS
- A method for systematic discovery of adverse drug events from clinical notes
- (2015) Guan Wang et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features
- (2015) A. Nikfarjam et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- The SIDER database of drugs and side effects
- (2015) Michael Kuhn et al. NUCLEIC ACIDS RESEARCH
- Systems Pharmacology Augments Drug Safety Surveillance
- (2015) T Lorberbaum et al. CLINICAL PHARMACOLOGY & THERAPEUTICS
- Properties of AdeABC and AdeIJK Efflux Systems of Acinetobacter baumannii Compared with Those of the AcrAB-TolC System of Escherichia coli
- (2014) Etsuko Sugawara et al. ANTIMICROBIAL AGENTS AND CHEMOTHERAPY
- Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
- (2014) Feixiong Cheng et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
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- (2013) Ying Li et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Safety Monitoring in Clinical Trials
- (2013) Bin Yao et al. Pharmaceutics
- Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs
- (2012) Mei Liu et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures
- (2012) Liang-Chin Huang et al. PROTEOMICS
- A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports
- (2011) Nicholas P Tatonetti et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
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- (2011) Robert Tibshirani JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
- Predicting Adverse Drug Events Using Pharmacological Network Models
- (2011) A. Cami et al. Science Translational Medicine
- A side effect resource to capture phenotypic effects of drugs
- (2010) Michael Kuhn et al. Molecular Systems Biology
- PubChem as a Source of Polypharmacology
- (2009) Bin Chen et al. Journal of Chemical Information and Modeling
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