GSFM

GSFM transforms pharmacotranscriptomic profiles into genome-scale functional modules to quantitatively evaluate drug efficacy for in silico drug discovery.

Key Features:

• Multi-level Quantifiers: GSFM employs four biologically interpretable quantifiers: GSFM_Up, GSFM_Down, GSFM_ssGSEA, and GSFM_TF to comprehensively evaluate multi-dimension activities of each functional module at gene-level, pathway-level, and transcriptional regulatory network-level.
• Data Transformation Strategy: GSFM converts noisy and potentially unreliable gene expression data into a more dependable functional module active matrix.
• Robustness and Accuracy: GSFM significantly outperforms other methods in both robustness and accuracy.
• Drug Efficacy Representation: RSGSFM shows positive correlation with drug efficacy and can serve as a representative measure of drug efficacy.

Scientific Applications:

• Cancer Drug Discovery: GSFM identified WYE-354 as a candidate therapeutic agent for breast-invasive carcinoma, perhexiline for lung adenocarcinoma, and NTNCB for castration-resistant prostate cancer, with all compounds validated through in vitro and in vivo experiments showing potent anti-tumor effects.

Methodology:

GSFM uses a data transformation strategy to convert pharmacotranscriptomic profiles into a functional module active matrix, employing four quantifiers (GSFM_Up, GSFM_Down, GSFM_ssGSEA, and GSFM_TF) that evaluate activities at gene-level, pathway-level, and transcriptional regulatory network-level.