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Drug Interactions

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A Drug Repurposing Method Based on Drug-Drug Interaction Networks and Using Energy Model Layouts.

Methods in molecular biology (Clifton, N.J.)
Complex network representations of reported drug-drug interactions foster computational strategies that can infer pharmacological functions which, in turn, create incentives for drug repositioning. Here, we use Gephi (a platform for complex network v...

A chronological pharmacovigilance network analytics approach for predicting adverse drug events.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to predict adverse drug events (ADEs) based on the drugs' similarities in terms of the proteins they t...

Nonlinear System Identification Based on Convolutional Neural Networks for Multiple Drug Interactions.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In heart failure patients, hemodynamics can be regulated by therapeutic drugs. Although the cardiovascular responses to these drugs usually include nonlinearity and drug interactions, it is difficult to identify the characteristics of the dynamics un...

Modeling polypharmacy side effects with graph convolutional networks.

Bioinformatics (Oxford, England)
MOTIVATION: The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Po...

DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches.

Bioinformatics (Oxford, England)
MOTIVATION: Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate.

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.