Adverse drug reaction discovery using a tumor-biomarker knowledge graph

M Wang, X Ma, J Si, H Tang, H Wang, T Li… - Frontiers in …, 2021 - frontiersin.org
Adverse drug reactions (ADRs) are a major public health concern, and early detection is
crucial for drug development and patient safety. Together with the increasing availability of …

Rapid assessment of adverse drug reactions by statistical solution of gene association network

YP Xiang, K Liu, XY Cheng, C Cheng… - … ACM transactions on …, 2014 - ieeexplore.ieee.org
Adverse drug reaction (ADR) is a common clinical problem, sometimes accompanying with
high risk of mortality and morbidity. It is also one of the major factors that lead to failure in …

Predicting putative adverse drug reaction related proteins based on network topological properties

Y Jiang, Y Li, Q Kuang, L Ye, Y Wu, L Yang, M Li - Analytical Methods, 2014 - pubs.rsc.org
Adverse drug reactions (ADRs) are one of the main issues restraining the development and
clinical applications of new drugs. Owing to complicated molecular mechanisms of ADRs …

Prediction of adverse drug reaction linked to protein targets using network-based information and machine learning

C Galletti, J Aguirre-Plans, B Oliva… - Frontiers in …, 2022 - frontiersin.org
Drug discovery attrition rates, particularly at advanced clinical trial stages, are high because
of unexpected adverse drug reactions (ADR) elicited by novel drug candidates. Predicting …

Drug‐target‐ADR Network and Possible Implications of Structural Variants in Adverse Events

B Dafniet, N Cerisier, K Audouze… - Molecular …, 2020 - Wiley Online Library
Adverse drug reactions (ADRs) are of major concern in drug safety. However, due to the
biological complexity of human systems, understanding the underlying mechanisms …

Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases

K Raja, M Patrick, JT Elder, LC Tsoi - Scientific reports, 2017 - nature.com
Adverse drug reactions (ADRs) pose critical public health issues, affecting over 6% of
hospitalized patients. While knowledge of potential drug-drug interactions (DDI) is …

Investigating ADR mechanisms with explainable AI: a feasibility study with knowledge graph mining

E Bresso, P Monnin, C Bousquet, FÉ Calvier… - BMC medical informatics …, 2021 - Springer
Abstract Background Adverse drug reactions (ADRs) are statistically characterized within
randomized clinical trials and postmarketing pharmacovigilance, but their molecular …

Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models

E Muñoz, V Nováček… - Briefings in …, 2019 - academic.oup.com
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 …

Drug-disease graph: predicting adverse drug reaction signals via graph neural network with clinical data

H Kwak, M Lee, S Yoon, J Chang, S Park… - Advances in Knowledge …, 2020 - Springer
Abstract Adverse Drug Reaction (ADR) is a significant public health concern world-wide.
Numerous graph-based methods have been applied to biomedical graphs for predicting …

Large-scale identification of adverse drug reaction-related proteins through a random walk model

X Chen, H Shi, F Yang, L Yang, Y Lv, S Wang, E Dai… - Scientific reports, 2016 - nature.com
Adverse drug reactions (ADRs) are responsible for drug failure in clinical trials and affect life
quality of patients. The identification of ADRs during the early phases of drug development is …