Claudio Monteiro

This paper demonstrates using AI to improve reproducibility of a mathematical model of Retinitis Pigmentosa by converting equations from PDF to SBML and simulating them.

The paper introduces four empirical methods to validate time-to-event models in EGFR+ lung adenocarcinoma, considering uncertainty and variability, and shows their context-dependent effectiveness.

The paper describes a calibration strategy for multiscale mechanistic models using heterogeneous data, demonstrating its application to a lung adenocarcinoma model.

This paper enhances Boolean networks with continuous logical operators and tunable edges to improve qualitative modeling of biological networks without requiring quantitative data.