Francisco J. Planes

The paper introduces genetic Minimal Intervention Sets (gMISs) to identify lethal genetic interactions beyond synthetic lethality in human cells and demonstrates their application in cancer research.

gMCSpy is a Python package for calculating genetic minimal cut sets in genome-scale metabolic models, offering improved accuracy and speed over existing tools.

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.

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.

This paper introduces AGREDA, an extended gut microbiota metabolism model for personalized nutrition, showing improved accuracy in predicting microbial output metabolites and identifying metabolic differences in Spanish children subgroups.

The paper identifies common issues in how gene-protein-reaction (GPR) rules and gene expression data are handled in context-specific metabolic models and evaluates their impact on gene essentiality analysis.