Ariadna Acedo-Terrades

The paper develops a computational framework using optimal transport to analyze macrophage state transitions in atherosclerosis, identifying origin-dependent plasticity mechanisms and shared regulatory circuits in inflammation and fibrosis.

This paper identifies MED13L and RERE as genes regulated by EWSR1::FLI1 in Ewing sarcoma, showing their role in altering translation and alternative splicing, and demonstrates RBM39 inhibition as a therapeutic strategy.

This study identifies molecular programs in macrophages stimulated by tumor-derived cues, linking them to clinically relevant TAM states and functions.

This study shows that EWS-FLI1 expression in human embryonic mesenchymal stem cells leads to an ES-like transcriptome and tumor formation, despite not preferentially binding promoters.

The paper develops a machine learning model using transcriptomic data to predict chemotherapy response in muscle-invasive bladder cancer with high accuracy.