Edurne San José‐Eneriz

gMCStool identifies metabolic vulnerabilities in cancer by predicting synthetic lethal interactions using genome-scale metabolic networks and RNA-seq data.

BOSO is a novel feature selection algorithm for linear regression that outperforms existing methods in high-dimensional datasets, demonstrated through drug sensitivity prediction in cancer.

The paper improves prediction of lethal gene dependencies using the HUGE method, achieving 75x higher statistical power than existing methods in cancer genomics.

This paper introduces a network-based method to predict synthetic lethality in cancer metabolism by integrating genetic interactions and nutrient environments, identifying potential vulnerabilities in tumor cells.