Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings
Background Current approaches to identifying drug-drug interactions (DDIs), include safety
studies during drug development and post-marketing surveillance after approval, offer …
studies during drug development and post-marketing surveillance after approval, offer …
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 …
[PDF][PDF] KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction.
Drug-drug interaction (DDI) prediction is a challenging problem in pharmacology and
clinical application, and effectively identifying potential D-DIs during clinical trials is critical …
clinical application, and effectively identifying potential D-DIs during clinical trials is critical …
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 …
Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining
Background The pharmaceutical field faces a significant challenge in validating drug target
interactions (DTIs) due to the time and cost involved, leading to only a fraction being …
interactions (DTIs) due to the time and cost involved, leading to only a fraction being …
Drug–drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings
Y Dai, C Guo, W Guo, C Eickhoff - Briefings in bioinformatics, 2021 - academic.oup.com
An interaction between pharmacological agents can trigger unexpected adverse events.
Capturing richer and more comprehensive information about drug–drug interactions (DDIs) …
Capturing richer and more comprehensive information about drug–drug interactions (DDIs) …
[HTML][HTML] Predicting polypharmacy side-effects using knowledge graph embeddings
V Nováček, SK Mohamed - AMIA Summits on Translational …, 2020 - ncbi.nlm.nih.gov
Polypharmacy is the use of drug combinations and is commonly used for treating complex
and terminal diseases. Despite its effectiveness in many cases, it poses high risks of …
and terminal diseases. Despite its effectiveness in many cases, it poses high risks of …
Discovering protein drug targets using knowledge graph embeddings
SK Mohamed, V Nováček, A Nounu - Bioinformatics, 2020 - academic.oup.com
Motivation Computational approaches for predicting drug–target interactions (DTIs) can
provide valuable insights into the drug mechanism of action. DTI predictions can help to …
provide valuable insights into the drug mechanism of action. DTI predictions can help to …
[HTML][HTML] Learning Drug-Disease-Target Embedding (DDTE) from knowledge graphs to inform drug repurposing hypotheses
We aimed to develop and validate a new graph embedding algorithm for embedding drug-
disease-target networks to generate novel drug repurposing hypotheses. Our model …
disease-target networks to generate novel drug repurposing hypotheses. Our model …
Large-scale structural and textual similarity-based mining of knowledge graph to predict drug–drug interactions
Abstract Drug–Drug Interactions (DDIs) are a major cause of preventable Adverse Drug
Reactions (ADRs), causing a significant burden on the patients' health and the healthcare …
Reactions (ADRs), causing a significant burden on the patients' health and the healthcare …