Enrique Medina‐Acosta

RCDome curates a human gene atlas of regulated cell death by integrating multiple databases and validating gene-RCD associations through literature and AI-assisted methods.

This paper develops a Pan-Cancer Multi-Omic SuperLearner to map non-linear Survival Topologies of regulated cell death, improving risk prediction beyond linear models.

The paper introduces a geometric framework to represent multi-omic signatures as multidimensional objects, revealing discordance and structural patterns in cancer metabolic circuitries.

This study develops a multi-omic framework to map convergent and divergent metabolic regulatory circuitries across cancers, revealing context-dependent regulatory patterns.

The paper develops a multi-optosis model integrating 25 RCD forms across 33 cancer types to discover candidate biomarkers and therapy targets using multi-omic and phenotypic data.

The paper identifies differential haplotype expression in MHC class I genes during SARS-CoV-2 infection in human lung cell lines, showing allele-specific switching linked to epitope binding affinity.

The paper identifies sex- and age-related differences in mitochondrial protein-coding gene expression across cancers and links these to prognosis and immune infiltration.