J. Llopis-Lorente

Considering population variability of electrophysiological models improves the in silico assessment of drug-induced torsadogenic risk

J. Saiz, B. Trénor, J. Llopis-Lorente, 2022, Comput. Methods Programs Biomed..

Omecamtiv Mecarbil Improves Contraction Behaviour in a 3D Electromechanical Tissue Model of Heart Failure

J. Saiz, B. Trénor, H. Arevalo, 2022, 2022 Computing in Cardiology (CinC).

In Silico Classifiers for the Assessment of Drug Proarrhythmicity

Beatriz Trénor, Javier Saiz, Julio Gomis-Tena, 2020, J. Chem. Inf. Model..

Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models

J. Saiz, B. Trénor, J. Llopis-Lorente, 2023, Archives of Toxicology.

Application of machine learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia

J. Saiz, B. Trénor, J. Llopis-Lorente, 2023, Comput. Methods Programs Biomed..