[PDF][PDF] MultiGML: Multimodal graph machine learning for prediction of adverse drug events
Adverse drug events constitute a major challenge for the success of clinical trials. Several
computational strategies have been suggested to estimate the risk of adverse drug events in …
computational strategies have been suggested to estimate the risk of adverse drug events in …
[HTML][HTML] A knowledge graph embedding approach for polypharmacy side effects prediction
J Kim, M Shin - Applied Sciences, 2023 - mdpi.com
Predicting the side effects caused by drug combinations may facilitate the prescription of
multiple medications in a clinical setting. So far, several prediction models of multidrug side …
multiple medications in a clinical setting. So far, several prediction models of multidrug side …
[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 …
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 …
[HTML][HTML] 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 …
Effective knowledge graph embeddings based on multidirectional semantics relations for polypharmacy side effects prediction
Motivation Polypharmacy is the combined use of drugs for the treatment of diseases.
However, it often shows a high risk of side effects. Due to unnecessary interactions of …
However, it often shows a high risk of side effects. Due to unnecessary interactions of …
Knowledge graph completion to predict polypharmacy side effects
B Malone, A García-Durán, M Niepert - … on data integration in the life …, 2018 - Springer
The polypharmacy side effect prediction problem considers cases in which two drugs taken
individually do not result in a particular side effect; however, when the two drugs are taken in …
individually do not result in a particular side effect; however, when the two drugs are taken in …
Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models
Timely identification of adverse drug reactions (ADRs) is highly important in the domains of
public health and pharmacology. Early discovery of potential ADRs can limit their effect on …
public health and pharmacology. Early discovery of potential ADRs can limit their effect on …
[HTML][HTML] Adverse drug event prediction using noisy literature-derived knowledge graphs: algorithm development and validation
S Dasgupta, A Jayagopal, ALJ Hong… - JMIR Medical …, 2021 - medinform.jmir.org
Background: Adverse drug events (ADEs) are unintended side effects of drugs that cause
substantial clinical and economic burdens globally. Not all ADEs are discovered during …
substantial clinical and economic burdens globally. Not all ADEs are discovered during …
[HTML][HTML] Investigating ADR mechanisms with explainable AI: a feasibility study with knowledge graph mining
Abstract Background Adverse drug reactions (ADRs) are statistically characterized within
randomized clinical trials and postmarketing pharmacovigilance, but their molecular …
randomized clinical trials and postmarketing pharmacovigilance, but their molecular …