PharmKG: a dedicated knowledge graph benchmark for bomedical data mining

S Zheng, J Rao, Y Song, J Zhang, X Xiao… - Briefings in …, 2021 - academic.oup.com
Biomedical knowledge graphs (KGs), which can help with the understanding of complex
biological systems and pathologies, have begun to play a critical role in medical practice …

Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction

X Lin, L Dai, Y Zhou, ZG Yu, W Zhang… - Briefings in …, 2023 - academic.oup.com
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph
learning models have established their usefulness in biomedical applications, especially in …

DFinder: a novel end-to-end graph embedding-based method to identify drug–food interactions

T Wang, J Yang, Y Xiao, J Wang, Y Wang… - …, 2023 - academic.oup.com
Abstract Motivation Drug–food interactions (DFIs) occur when some constituents of food
affect the bioaccessibility or efficacy of the drug by involving in drug pharmacodynamic …

BridgeDPI: a novel graph neural network for predicting drug–protein interactions

Y Wu, M Gao, M Zeng, J Zhang, M Li - Bioinformatics, 2022 - academic.oup.com
Motivation Exploring drug–protein interactions (DPIs) provides a rapid and precise approach
to assist in laboratory experiments for discovering new drugs. Network-based methods …

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

MA Thafar, RS Olayan, H Ashoor, S Albaradei… - Journal of …, 2020 - Springer
In silico prediction of drug–target interactions is a critical phase in the sustainable drug
development process, especially when the research focus is to capitalize on the …

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

DM Bean, H Wu, E Iqbal, O Dzahini, ZM Ibrahim… - Scientific reports, 2017 - nature.com
Unknown adverse reactions to drugs available on the market present a significant health risk
and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning …

DDI-GCN: drug-drug interaction prediction via explainable graph convolutional networks

Y Zhong, H Zheng, X Chen, Y Zhao, T Gao… - Artificial Intelligence in …, 2023 - Elsevier
Drug-drug interactions (DDI) may lead to unexpected side effects, which is a growing
concern in both academia and industry. Many DDIs have been reported, but the underlying …

DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches

RS Olayan, H Ashoor, VB Bajic - Bioinformatics, 2018 - academic.oup.com
Motivation Finding computationally drug–target interactions (DTIs) is a convenient strategy
to identify new DTIs at low cost with reasonable accuracy. However, the current DTI …

Predicting drug–disease associations through layer attention graph convolutional network

Z Yu, F Huang, X Zhao, W Xiao… - Briefings in …, 2021 - academic.oup.com
Background: Determining drug–disease associations is an integral part in the process of
drug development. However, the identification of drug–disease associations through wet …

Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties

F Cheng, Z Zhao - Journal of the American Medical Informatics …, 2014 - academic.oup.com
Abstract Objective Drug–drug interactions (DDIs) are an important consideration in both
drug development and clinical application, especially for co-administered medications …