AIMC Topic: Drug Interactions

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Drug-drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths.

Bioinformatics (Oxford, England)
MOTIVATION: Adverse events resulting from drug-drug interactions (DDI) pose a serious health issue. The ability to automatically extract DDIs described in the biomedical literature could further efforts for ongoing pharmacovigilance. Most of neural n...

SuperDRUG2: a one stop resource for approved/marketed drugs.

Nucleic acids research
Regular monitoring of drug regulatory agency web sites and similar resources for information on new drug approvals and changes to legal status of marketed drugs is impractical. It requires navigation through several resources to find complete informa...

Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives.

Methods in molecular biology (Clifton, N.J.)
Identification of drug targets and drug target interactions are important steps in the drug-discovery pipeline. Successful computational prediction methods can reduce the cost and time demanded by the experimental methods. Knowledge of putative drug ...

Multi-label classifier based on histogram of gradients for predicting the anatomical therapeutic chemical class/classes of a given compound.

Bioinformatics (Oxford, England)
MOTIVATION: Given an unknown compound, is it possible to predict its Anatomical Therapeutic Chemical class/classes? This is a challenging yet important problem since such a prediction could be used to deduce not only a compound's possible active ingr...

Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To compare 3 commercial knowledge bases (KBs) used for detection and avoidance of potential drug-drug interactions (DDIs) in clinical practice.

Formalizing Evidence Type Definitions for Drug-Drug Interaction Studies to Improve Evidence Base Curation.

Studies in health technology and informatics
In this research we aim to demonstrate that an ontology-based system can categorize potential drug-drug interaction (PDDI) evidence items into complex types based on a small set of simple questions. Such a method could increase the transparency and r...

Detecting Signals of Interactions Between Warfarin and Dietary Supplements in Electronic Health Records.

Studies in health technology and informatics
Drug and supplement interactions (DSIs) have drawn widespread attention due to their potential to affect therapeutic response and adverse event risk. Electronic health records provide a valuable source where the signals of DSIs can be identified and ...

Exploring convolutional neural networks for drug-drug interaction extraction.

Database : the journal of biological databases and curation
Drug-drug interaction (DDI), which is a specific type of adverse drug reaction, occurs when a drug influences the level or activity of another drug. Natural language processing techniques can provide health-care professionals with a novel way of redu...

[Prevalence of potential drug-drug interactions involving antiretroviral drugs in Buenos Aires, Argentina].

Revista chilena de infectologia : organo oficial de la Sociedad Chilena de Infectologia
INTRODUCTION: Antiretroviral agents (ARVs) have a high potential for drug interactions. However, the prevalence and risk factors for clinically significant drug-drug interactions (CSDDIs) with ARVs from Latin American countries is unknown.

Classification of Human Pregnane X Receptor (hPXR) Activators and Non-Activators by Machine Learning Techniques: A Multifaceted Approach.

Combinatorial chemistry & high throughput screening
The Human Pregnane X Receptor (hPXR) is a regulator of drug metabolising enzymes (DME) and efflux transporters (ET). The prediction of hPXR activators and non-activators has pharmaceutical importance to predict the multiple drug resistance (MDR) and ...