RANKOR: Direct Drug Prioritization from Bulk and Single-Cell Transcriptomic Signatures (opens in new tab)

Background Prioritizing therapeutics from transcriptomic data remains a key challenge in precision medicine. Signature reversal approaches, most commonly implemented through Gene Set Enrichment Analysis (GSEA), have been widely used to match disease signatures to candidate drugs. However, enrichment-based methods can be sensitive to noise and are restricted to previously profiled compounds Methods We developed RANKOR, a machine-learning framework designed to rank candidate drugs directly from...