Exploring Diachronic Changes of Biomedical Knowledge Using Distributed Concept Representations

This study expands the Drug Ontology to include therapeutic indications for resistant hypertension, malaria, and opioid abuse research, providing a framework for additional drug use representations beyond their original design.

This study introduced a pre-formal ontology matching approach using 39 identified dimensions to integrate drug information, improving database normalization and supporting complex use cases.

This study reported that the Computational Analysis of Novel Drug Opportunities platform effectively uses integrated biological data, including side effects and pathways, to generate potential drug candidates for colon cancer and migraine disorders.

This study proposes an AI-based method, DRGCC, using GraphSAGE and clustering constraints to predict associations between drugs and diseases, demonstrating reliable predictive performance that may aid drug repositioning efforts, including exploring drugs for COVID-19 treatment.

This study suggests that using a new computational method to mine social media for side-effect data may enhance drug repositioning by recovering known and trial drug indications effectively.