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1.
Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models.
Arch Toxicol;
97(10): 2721-2740, 2023 10.
Artículo
en Inglés
| MEDLINE | ID: mdl-37528229
2.
In Silico Classifiers for the Assessment of Drug Proarrhythmicity.
J Chem Inf Model;
60(10): 5172-5187, 2020 10 26.
Artículo
en Inglés
| MEDLINE | ID: mdl-32786710
3.
Impact of Epicardial Adipose Tissue on Infarct Size and Left Ventricular Systolic Function in Patients with Anterior ST-Segment Elevation Myocardial Infarction.
Diagnostics (Basel);
14(4)2024 Feb 07.
Artículo
en Inglés
| MEDLINE | ID: mdl-38396407
4.
Application of machine learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia.
Comput Methods Programs Biomed;
230: 107345, 2023 Mar.
Artículo
en Inglés
| MEDLINE | ID: mdl-36689808
5.
Combining pharmacokinetic and electrophysiological models for early prediction of drug-induced arrhythmogenicity.
Comput Methods Programs Biomed;
242: 107860, 2023 Dec.
Artículo
en Inglés
| MEDLINE | ID: mdl-37844488
6.
Considering population variability of electrophysiological models improves the in silico assessment of drug-induced torsadogenic risk.
Comput Methods Programs Biomed;
221: 106934, 2022 Jun.
Artículo
en Inglés
| MEDLINE | ID: mdl-35687995
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