Guillermo Prol-Castelo

This paper uses generative and symbolic AI to forecast kidney cancer progression from cross-sectional data, providing interpretable models and molecular insights.

This paper reviews the use of deep representation learning, particularly VAEs, in cancer omics to model temporal processes, highlighting limitations in capturing cancer progression dynamics.

This study uses synthetic data generation and explainable AI to uncover an intermediate subgroup in medulloblastoma, highlighting treatment disparities and supporting the continuum hypothesis between Groups 3 and 4.