Research graph
References from A benchmark study comparing deep learning and path analysis approaches for multi-class link prediction on a disease-specific literature knowledge graph. Local targets link to admitted publications; unresolved targets remain external evidence.
Mechanisms of drug interactions: Pharmacodynamics and pharmacokinetics
10.1016/j.mpaic.2014.04.005 · 2014 · External reference
Pharmacokinetic and pharmacodynamic drug–drug interactions: Research methods and applications
10.3390/metabo13080897 · 2023 · External reference
A review of knowledge graph completion
10.3390/info13080396 · 2022 · External reference
DrugBank 6.0: the DrugBank knowledgebase for 2024
10.1093/nar/gkad976 · 2014 · External reference
Predicting multi-type drug-drug interactions using a disease-specific knowledge graph
2025 · External reference
Drug-drug interactions prediction based on deep learning and knowledge graph: A review
10.1016/j.isci.2024.109148 · 2024 · External reference
Translating embeddings for modeling multi-relational data
2013 · External reference
Holographic embeddings of knowledge graphs
2016 · External reference
Unresolved reference
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Unresolved reference
2014 · External reference
Complex embeddings for simple link prediction
2016 · External reference
Unresolved reference
2017 · External reference
Drug-drug interaction prediction on a biomedical literature knowledge graph
2020 · External reference
Integrating heterogeneous knowledge graphs into drug–drug interaction extraction from the literature
10.1093/bioinformatics/btac754 · 2023 · External reference
BioDKG–DDI: predicting drug–drug interactions based on drug knowledge graph fusing biochemical information
10.1093/bfgp/elac004 · 2022 · External reference
A biomedical knowledge graph-based method for drug–drug interactions prediction through combining local and global features with deep neural networks
10.1093/bib/bbac363 · 2022 · External reference
Modeling polypharmacy side effects with graph convolutional networks
10.1093/bioinformatics/bty294 · 2018 · External reference
DDI-GCN: Drug-drug interaction prediction via explainable graph convolutional networks
10.1016/j.artmed.2023.102640 · 2023 · External reference
Prediction of drug-drug interaction using an attention-based graph neural network on drug molecular graphs
10.3390/molecules27093004 · 2022 · External reference
10.1145/3485447.3511923
10.1145/3485447.3511923 · External reference
On the road to explainable AI in drug-drug interactions prediction: A systematic review
10.1016/j.csbj.2022.04.021 · 2022 · External reference
Knowledge graph embeddings in the biomedical domain: Are they useful? a look at link prediction, rule learning, and downstream polypharmacy tasks
10.1093/bioadv/vbae097 · 2024 · External reference
Gnnexplainer: Generating explanations for graph neural networks
2019 · External reference
Biomedical knowledge graph embedding with capsule network for multi-label drug-drug interaction prediction
2023 · External reference
MCFF-MTDDI: multi-channel feature fusion for multi-typed drug–drug interaction prediction
10.1093/bib/bbad215 · 2023 · External reference
iASiS open data graph: Automated semantic integration of disease-specific knowledge
2020 · External reference
Broad-coverage biomedical relation extraction with SemRep
2020 · External reference
Unresolved reference
2018 · External reference
Modeling polypharmacy side effects with graph convolutional networks
10.1093/bioinformatics/bty294 · 2018 · External reference
Modeling relational data with graph convolutional networks
2018 · External reference
SMOTE: Synthetic minority over-sampling technique
10.1613/jair.953 · 2002 · External reference
Comparing methods for drug–gene interaction prediction on the biomedical literature knowledge graph: performance versus explainability
10.1186/s12859-023-05373-2 · 2023 · External reference
Unresolved reference
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