Leveraging Large-Scale Perturbome Data for Complex Disease Target Discovery- Sang-Min Park

Complex diseases, such as cancer, sarcopenia, and immune disorders, arise from abnormalities in multiple genes and pathways, posing significant challenges to conventional single-target drug discovery strategies. To address this, we developed a perturbome-based analytical framework that integrates transcriptomic signatures, network pharmacology, and machine learning to identify effective therapeutic candidates. Central to this approach is the KORE-Map (Korean Medicine Omics Resource Extension Map), a systematically curated transcriptomic repository of herbal medicine perturbations in diverse cellular and disease contexts. Using KORE-Map, we reconstructed perturbome landscapes of traditional prescriptions such as Bojungikki-tang and Jakyak-gamcho-tang, as well as natural compounds including ginsenosides and licochalcone B. By integrating differential expression, pathway activity, and synergic index modeling, we demonstrated how perturbome data can reveal druggable axes for overcoming resistance to targeted and immune therapies in cancer, attenuating muscle atrophy, and modulating inflammatory responses. Importantly, perturbome-guided candidate prioritization was validated through multi-omics profiling and functional assays, underscoring its translational value. Our findings highlight perturbome data analysis as a powerful strategy for navigating the complexity of disease biology and accelerating drug discovery.