DREIMT

DREIMT generates hypotheses and prioritizes drugs by analyzing transcriptomics-derived gene expression signatures to identify compounds that modulate immune cell activity for applications in oncology and autoimmune disease research.

Key Features:

• Hypothesis Generation: Analyzes drug profiles against a comprehensive database of immune gene expression signatures to suggest immunomodulatory hypotheses.
• Drug Prioritization Analysis: Ranks and identifies significant drugs targeting up to 70 different immune cell subtypes for downstream validation.
• Curated Database Integration: Integrates 4,960 drug profiles and approximately 2,600 immune gene expression signatures to support comparisons.
• User-Supplied Data Analysis: Accepts researcher-provided gene expression signatures and compares them to the curated resource to suggest candidate drugs.
• Comprehensive Output: Produces drug-signature association scores, false discovery rates (FDRs), plots, and results tables for interpretation.

Scientific Applications:

• Oncology: Identifies drugs that may modulate immune cell activity relevant to cancer therapy research.
• Immunology: Supports exploration of compounds that influence immune responses in autoimmune and inflammatory conditions.
• Drug Repurposing: Enables repurposing efforts by matching existing drug profiles to immune gene expression signatures.

Methodology:

Performs systematic analysis of 4,960 drug profiles against a curated collection of ~2,600 immune gene expression signatures derived from transcriptomics to compute drug-signature association scores and false discovery rates (FDRs) for prioritization across up to 70 immune cell subtypes.