Pharmacovigilance and Adverse Drug Reactions

Contrastive learning of adverse events to provide effective and interpretable vector representations for machine-assisted pharmacovigilance

This paper develops interpretable adverse event vector representations using contrastive learning for pharmacovigilance, outperforming existing methods in drug-event causality prediction.

A genomic-led strategy to anticipate drug safety effects

The paper uses Mendelian Randomization to predict adverse drug reactions by linking genetic variants to gene expression and protein abundance across large datasets.

Human genetic evidence enriched for side effects of approved drugs

The paper shows that human genetic evidence can identify drug side effects by linking drug targets to traits similar to the side effects.

DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis

DREAMER identifies molecular mechanisms of adverse drug reactions and disease phenotypes using network-based analysis, revealing key proteins and pathways involved in various conditions.

Mining Archive.org's Twitter Stream Grab for Pharmacovigilance Research Gold

The paper demonstrates mining 9.4 billion tweets from archive.org for pharmacovigilance research using a drug-term dictionary and machine learning models to filter relevant tweets.

RedMed: Extending drug lexicons for social media applications

RedMed improves drug mention detection in social media by using a word embedding model trained on Reddit data, capturing more mentions than exact string matches.

Assessment of the cardiovascular adverse effects of drug-drug interactions through a combined analysis of spontaneous reports and predicted drug-target interactions

The paper develops a computational approach to assess cardiovascular adverse effects of drug-drug interactions using spontaneous reports and predicted drug-target interactions, identifying dangerous drug combinations.