Ramón Díaz‐Uriarte

The paper introduces HyperMk2, a method to efficiently model the coevolution of multiple binary characters by reducing state space, enabling analysis of larger systems than previous methods.

This paper introduces a hypercubic Mk model framework to infer accumulation dynamics with reversibility in disease, cancer, and evolutionary contexts using synthetic and real data.

HyperTraPS-CT infers and predicts accumulation pathways in complex systems with flexible data and model structures, demonstrating its application to cancer and tuberculosis resistance.

This paper develops a spatially explicit agent-based model to simulate tumor growth and adaptive therapy, showing that mutation order restrictions affect genotype predictions and therapy outcomes.

EvAM-Tools provides an R package and web app for building and analyzing evolutionary models of cancer progression and event accumulation.

This paper investigates short-term tumor evolution prediction using cancer progression models, finding that specific genotype and fitness landscape characteristics improve prediction accuracy.

This study evaluates four cancer progression models to predict tumor progression paths and estimates evolutionary unpredictability, finding poor performance under multi-peaked fitness landscapes and unreliable predictions in most cancer datasets.

This paper shows that cancer progression models (CPMs) perform poorly in the presence of reciprocal sign epistasis, leading to inconsistent DAGs from different fitness landscapes.