ITCM

ITCM leverages gene expression profiles to perform pharmacotranscriptomic computational screening of Traditional Chinese Medicine (TCM) active ingredients to identify candidate drug–disease associations and elucidate mechanisms of action.

Key Features:

• Gene expression–based screening: Performs computational drug screening using gene expression profiles derived from high-throughput experiments.
• High-throughput dataset: Contains high-throughput experimental data covering 496 representative TCM active ingredients.
• Transcriptome-based multi-scale analyses: Uses transcriptome-level multi-scale analyses to investigate mechanisms of action of active ingredients.
• TCM-Query signature strategy: Integrates six state-of-the-art signature search methods into the TCM-Query strategy for signature-based matching.
• Prior-knowledge–independent pairing: Enables discovery of drug–disease pairings without requiring prior drug- or disease-specific knowledge.
• Literature mining: Incorporates literature mining to collect related TCM resources and contextual information.
• Empirical case analyses: Demonstrates application through analyses of bavachinin with respect to prostate cancer and COVID-19.

Scientific Applications:

• Active ingredient identification: Identification and prioritization of pharmacological active ingredients from TCM using transcriptomic signatures.
• Drug–disease association discovery: Computational discovery of candidate drug–disease pairings based on signature similarity.
• Mechanistic inference: Elucidation of mechanisms of action via transcriptome-based multi-scale analyses.
• Case-study evaluation: Targeted analysis of specific compounds such as bavachinin for relevance to prostate cancer and COVID-19.

Methodology:

Uses gene expression profiles and high-throughput experimental data on 496 active ingredients, applies transcriptome-based multi-scale analyses, integrates six signature search methods within TCM-Query, and incorporates literature mining.