Machine learning techniques are being increasingly used in the analysis of clinical and
omics data. This increase is primarily due to the advancements in Artificial intelligence (AI) …

Traditional machine learning methods used to detect the side effects of drugs pose
significant challenges as feature engineering processes are labor-intensive, expert …

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and
improve drug safety assessment. With an increasing number of biological and medical data …

Drug-drug interactions (DDIs) can trigger unexpected pharmacological effects, including
adverse drug events (ADEs), with causal mechanisms often unknown. Recently, deep …

Adverse drug reactions (ADRs) and drug-induced toxicity are major challenges in drug
discovery, threatening patient safety and dramatically increasing healthcare expenditures …

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 …

Adverse drug events (ADEs) are responsible for a significant number of hospital admissions
and fatalities. Machine learning models have been developed to assess individual patient …

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 …

Background Genomic variations are associated with the metabolism and the occurrence of
adverse reactions of many therapeutic agents. The polymorphisms on over 2000 locations of …

Adverse drug reactions (ADRs) pose critical public health issues, affecting over 6% of
hospitalized patients. While knowledge of potential drug-drug interactions (DDI) is …