A comprehensive survey of graph neural networks for knowledge graphs
The Knowledge graph, a multi-relational graph that represents rich factual information
among entities of diverse classifications, has gradually become one of the critical tools for …
among entities of diverse classifications, has gradually become one of the critical tools for …
Application of Artificial Intelligence in Drug–Drug Interactions Prediction: A Review
Y Zhang, Z Deng, X Xu, Y Feng… - Journal of chemical …, 2023 - ACS Publications
Drug–drug interactions (DDI) are a critical aspect of drug research that can have adverse
effects on patients and can lead to serious consequences. Predicting these events …
effects on patients and can lead to serious consequences. Predicting these events …
Attention-based knowledge graph representation learning for predicting drug-drug interactions
Drug–drug interactions (DDIs) are known as the main cause of life-threatening adverse
events, and their identification is a key task in drug development. Existing computational …
events, and their identification is a key task in drug development. Existing computational …
Label propagation prediction of drug-drug interactions based on clinical side effects
Drug-drug interaction (DDI) is an important topic for public health and thus attracts attention
from both academia and industry. Here we hypothesize that clinical side effects (SEs) …
from both academia and industry. Here we hypothesize that clinical side effects (SEs) …
SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization
Motivation Thanks to the increasing availability of drug–drug interactions (DDI) datasets and
large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using …
large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using …
An ai‐based prediction model for drug‐drug interactions in osteoporosis and Paget's diseases from smiles
The skeleton is one of the most important organs in the human body in assisting our motion
and activities; however, bone density attenuates gradually as we age. Among common bone …
and activities; however, bone density attenuates gradually as we age. Among common bone …
Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data
Abstract Background Drug-drug interactions (DDIs) are one of the major concerns in drug
discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions …
discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions …
Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
Abstract Objective Drug–drug interactions (DDIs) are an important consideration in both
drug development and clinical application, especially for co-administered medications …
drug development and clinical application, especially for co-administered medications …
Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-LSTM network
Interference between pharmacological substances can cause serious medical injuries.
Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases …
Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases …
Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network
Drug–drug interactions (DDIs) for emerging drugs offer possibilities for treating and
alleviating diseases, and accurately predicting these with computational methods can …
alleviating diseases, and accurately predicting these with computational methods can …