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In this study, researchers developed a computational method called iEdgePathDDA that prioritizes anticancer drug candidates by analyzing changes in gene interactions, demonstrating superior performance compared to existing methods across colorectal, breast, and lung cancer datasets.

This study found that using curated biomedical databases as training examples for information extraction tasks in Genome-Wide Association Studies can outperform cost-insensitive methods, demonstrating their potential use without expert annotation.

This study found that the WNMFDDA model effectively predicts drug-disease associations, achieving high accuracy in cross-validation and confirming most candidate associations through existing databases.

In this study, researchers explored drug repurposing as a potential strategy for treating psoriasis and identified Pioglitazone, Trimipramine, and Dimetindene as promising candidates for this indication, based on molecular docking and predictive algorithms.

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.