AIMC Topic: Drug Interactions

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Position-aware deep multi-task learning for drug-drug interaction extraction.

Artificial intelligence in medicine Mar 17, 2018
OBJECTIVE: A drug-drug interaction (DDI) is a situation in which a drug affects the activity of another drug synergistically or antagonistically when being administered together. The information of DDIs is crucial for healthcare professionals to prev...
Drug Interactions Drug-Related Side Effects and Adverse Reactions Data Mining Databases, Factual Deep Learning
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Drug drug interaction extraction from the literature using a recursive neural network.

PloS one Jan 26, 2018
Detecting drug-drug interactions (DDI) is important because information on DDIs can help prevent adverse effects from drug combinations. Since there are many new DDI-related papers published in the biomedical domain, manually extracting DDI informati...
Drug-Related Side Effects and Adverse Reactions Data Mining Humans Pharmacokinetics Publications Support Vector Machine Drug Interactions Natural Language Processing Neural Networks, Computer
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Dependency-based long short term memory network for drug-drug interaction extraction.

BMC bioinformatics Dec 28, 2017
BACKGROUND: Drug-drug interaction extraction (DDI) needs assistance from automated methods to address the explosively increasing biomedical texts. In recent years, deep neural network based models have been developed to address such needs and they ha...
Neural Networks, Computer Algorithms Statistics as Topic Drug Interactions Databases as Topic
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Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions.

Cell chemical biology Dec 21, 2017
Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity d...
Humans Drug Discovery Knowledge Bases Pharmaceutical Preparations Consensus Drug Interactions Drug Repositioning
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Physiological based pharmacokinetic modeling to estimate in vivo Ki of ketoconazole on renal P-gp using human drug-drug interaction study result of fesoterodine and ketoconazole.

Drug metabolism and pharmacokinetics Nov 15, 2017
This study was conducted to estimate in vivo inhibition constant (Ki) of ketoconazole on renal P-glycoprotein (P-gp) using human drug-drug interaction (DDI) study result of fesoterodine and ketoconazole. Fesoterodine is a prodrug which is extensively...
Models, Biological Cytochrome P-450 CYP3A Inhibitors ATP Binding Cassette Transporter, Subfamily B, Member 1 Kidney Cross-Over Studies Ketoconazole Humans Drug Interactions Benzhydryl Compounds Liver Male Muscarinic Antagonists
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An attention-based effective neural model for drug-drug interactions extraction.

BMC bioinformatics Oct 10, 2017
BACKGROUND: Drug-drug interactions (DDIs) often bring unexpected side effects. The clinical recognition of DDIs is a crucial issue for both patient safety and healthcare cost control. However, although text-mining-based systems explore various method...
Drug Interactions Databases as Topic Neural Networks, Computer Algorithms Support Vector Machine Publications Models, Theoretical Humans
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An Ameliorated Prediction of Drug-Target Interactions Based on Multi-Scale Discrete Wavelet Transform and Network Features.

International journal of molecular sciences Aug 16, 2017
The prediction of drug-target interactions (DTIs) via computational technology plays a crucial role in reducing the experimental cost. A variety of state-of-the-art methods have been proposed to improve the accuracy of DTI predictions. In this paper,...
Wavelet Analysis Models, Biological Drug Interactions Humans Support Vector Machine Predictive Value of Tests Animals
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Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes.

BMC bioinformatics Mar 1, 2017
BACKGROUND: Investigating and understanding drug-drug interactions (DDIs) is important in improving the effectiveness of clinical care. DDIs can occur when two or more drugs are administered together. Experimentally based DDI detection methods requir...
Databases, Factual Support Vector Machine Protein Isoforms Humans Cluster Analysis Drug Interactions Pharmaceutical Preparations Cytochrome P-450 Enzyme System
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Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports.

Artificial intelligence in medicine Feb 10, 2017
OBJECTIVE: Drug-drug interaction (DDI) is of serious concern, causing over 30% of all adverse drug reactions and resulting in significant morbidity and mortality. Early discovery of adverse DDI is critical to prevent patient harm. Spontaneous reporti...
Data Mining Adverse Drug Reaction Reporting Systems Drug Interactions Humans Machine Learning Drug-Related Side Effects and Adverse Reactions Bayes Theorem
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A Topic-modeling Based Framework for Drug-drug Interaction Classification from Biomedical Text.

AMIA ... Annual Symposium proceedings. AMIA Symposium Feb 10, 2017
Classification of drug-drug interaction (DDI) from medical literatures is significant in preventing medication-related errors. Most of the existing machine learning approaches are based on supervised learning methods. However, the dynamic nature of d...
Humans Machine Learning Drug Interactions Information Storage and Retrieval Algorithms
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