Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology
Abstract Background Adverse drug reactions (ADRs) are one of the leading causes of
morbidity and mortality in health care. Understanding which drug targets are linked to ADRs …
morbidity and mortality in health care. Understanding which drug targets are linked to ADRs …
Data-driven prediction of adverse drug reactions induced by drug-drug interactions
R Liu, MDM AbdulHameed, K Kumar, X Yuˆ… - BMC Pharmacology and …, 2017 - Springer
Background The expanded use of multiple drugs has increased the occurrence of adverse
drug reactions (ADRs) induced by drug-drug interactions (DDIs). However, such reactions …
drug reactions (ADRs) induced by drug-drug interactions (DDIs). However, such reactions …
A structure-based approach for mapping adverse drug reactions to the perturbation of underlying biological pathways
I Wallach, N Jaitly, R Lilien - PloS one, 2010 - journals.plos.org
Adverse drug reactions (ADR), also known as side-effects, are complex undesired
physiologic phenomena observed secondary to the administration of pharmaceuticals …
physiologic phenomena observed secondary to the administration of pharmaceuticals …
Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms
Small molecules are indispensable to modern medical therapy. However, their use may lead
to unintended, negative medical outcomes commonly referred to as adverse drug reactions …
to unintended, negative medical outcomes commonly referred to as adverse drug reactions …
Improving drug safety: From adverse drug reaction knowledge discovery to clinical implementation
Adverse drug reactions (ADRs) are a major public health concern, causing over 100,000
fatalities in the United States every year with an annual cost of $136 billion. Early detection …
fatalities in the United States every year with an annual cost of $136 billion. Early detection …
Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning
Objective Adverse drug reaction (ADR) can have dire consequences. However, our current
understanding of the causes of drug-induced toxicity is still limited. Hence it is of paramount …
understanding of the causes of drug-induced toxicity is still limited. Hence it is of paramount …
A survey on adverse drug reaction studies: data, tasks and machine learning methods
Motivation Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug
discovery. Recently, with the rapid increase of both clinical and non-clinical data, machine …
discovery. Recently, with the rapid increase of both clinical and non-clinical data, machine …
Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases
Adverse drug reactions (ADRs) pose critical public health issues, affecting over 6% of
hospitalized patients. While knowledge of potential drug-drug interactions (DDI) is …
hospitalized patients. While knowledge of potential drug-drug interactions (DDI) is …
Leveraging genetic interactions for adverse drug-drug interaction prediction
In light of increased co-prescription of multiple drugs, the ability to discern and predict drug-
drug interactions (DDI) has become crucial to guarantee the safety of patients undergoing …
drug interactions (DDI) has become crucial to guarantee the safety of patients undergoing …
Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs
Objective Adverse drug reaction (ADR) is one of the major causes of failure in drug
development. Severe ADRs that go undetected until the post-marketing phase of a drug …
development. Severe ADRs that go undetected until the post-marketing phase of a drug …