AIMC Topic: Drug Interactions

Clear Filters Showing 31 to 40 of 277 articles

A Molecular Fragment Representation Learning Framework for Drug-Drug Interaction Prediction.

Interdisciplinary sciences, computational life sciences Oct 9, 2024
The concurrent use of multiple drugs may result in drug-drug interactions, increasing the risk of adverse reactions. Hence, it is particularly crucial to propose computational methods for precisely identifying unknown drug-drug interactions, which is...
Humans Machine Learning Pharmaceutical Preparations Algorithms Drug Interactions Computational Biology
View on PubMed DOI

A Network Enhancement Method to Identify Spurious Drug-Drug Interactions.

IEEE/ACM transactions on computational biology and bioinformatics Oct 9, 2024
As medical safety and drug regulation gain heightened attention, the detection of spurious drug-drug interactions (DDI) has become key in healthcare. Although current research using graph neural networks (GNNs) to predict DDI has shown impressive res...
Computational Biology Drug Interactions Humans Neural Networks, Computer Algorithms
View on PubMed DOI

RECOMED: A comprehensive pharmaceutical recommendation system.

Artificial intelligence in medicine Sep 12, 2024
OBJECTIVES: To build datasets containing useful information from drug databases and recommend a list of drugs to physicians and patients with high accuracy by considering a wide range of features of people, diseases, and chemicals.
Neural Networks, Computer Algorithms Humans Databases, Pharmaceutical Natural Language Processing Deep Learning Drug Interactions
View on PubMed DOI

DSIL-DDI: A Domain-Invariant Substructure Interaction Learning for Generalizable Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems Aug 5, 2024
Drug-drug interactions (DDIs) trigger unexpected pharmacological effects in vivo, often with unknown causal mechanisms. Deep learning methods have been developed to better understand DDI. However, learning domain-invariant representations for DDI rem...
Neural Networks, Computer Algorithms Deep Learning Drug Interactions Humans
View on PubMed DOI

Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions.

IEEE transactions on neural networks and learning systems Aug 5, 2024
Predicting drug-drug interactions (DDIs) is the problem of predicting side effects (unwanted outcomes) of a pair of drugs using drug information and known side effects of many pairs. This problem can be formulated as predicting labels (i.e., side eff...
Algorithms Drug Interactions Humans Neural Networks, Computer Drug-Related Side Effects and Adverse Reactions
View on PubMed DOI

BiRNN-DDI: A Drug-Drug Interaction Event Type Prediction Model Based on Bidirectional Recurrent Neural Network and Graph2Seq Representation.

Journal of computational biology : a journal of computational molecular cell biology Jul 25, 2024
Research on drug-drug interaction (DDI) prediction, particularly in identifying DDI event types, is crucial for understanding adverse drug reactions and drug combinations. This work introduces a Bidirectional Recurrent Neural Network model for DDI ev...
Recurrent Neural Networks Drug Interactions Humans Neural Networks, Computer Algorithms Computational Biology
View on PubMed DOI

Gtie-Rt: A comprehensive graph learning model for predicting drugs targeting metabolic pathways in human.

Journal of bioinformatics and computational biology Jul 20, 2024
Drugs often target specific metabolic pathways to produce a therapeutic effect. However, these pathways are complex and interconnected, making it challenging to predict a drug's potential effects on an organism's overall metabolism. The mapping of dr...
Pharmaceutical Preparations Humans Models, Biological Drug Interactions Computational Biology Neural Networks, Computer Machine Learning Metabolic Networks and Pathways Algorithms
View on PubMed DOI

PT-KGNN: A framework for pre-training biomedical knowledge graphs with graph neural networks.

Computers in biology and medicine Jun 26, 2024
Biomedical knowledge graphs (KGs) serve as comprehensive data repositories that contain rich information about nodes and edges, providing modeling capabilities for complex relationships among biological entities. Many approaches either learn node fea...
Neural Networks, Computer Machine Learning Humans Natural Language Processing Drug Interactions Computational Biology
View on PubMed DOI

Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention.

Journal of translational medicine Jun 16, 2024
BACKGROUND: Accurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction be...
Humans Neural Networks, Computer Reproducibility of Results Drug Combinations Drug-Related Side Effects and Adverse Reactions Risk Assessment Drug Interactions Computer Graphics
View on PubMed DOI

Knowledge enhanced attention aggregation network for medicine recommendation.

Computational biology and chemistry May 23, 2024
The combination of deep learning and the medical field has recently achieved great success, particularly in recommending medicine for patients. However, patients' clinical records often contain repeated medical information that can significantly impa...
Deep Learning Drug Interactions Humans
View on PubMed DOI
Popular Topics
Recent Journals