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B-PO02-022 Combining simulation and machine learning to accurately predict arrhythmic risk in post-infarction patients In Heart Rhythm. Vol. 18. Boston, MA: Elsevier, 2021.
"Combined In-silico and Machine Learning Approaches Toward Predicting Arrhythmic Risk in Post-infarction Patients." Frontiers in physiology 12 (2021): 745349.
A Combined In-Silico and Machine Learning Approach towards Predicting Arrhythmic Risk in Post-Infarction Patients In Computing in Cardiology, Singapore., 2019.
A multiple kernel learning framework to investigate the relationship between ventricular fibrillation and first myocardial infarction In Functional Imaging and Modelling of the Heart, Edited by M. Pop and G. Wright. Springer, 2017.
From MR image to patient-specific simulation and population-based analysis:Tutorial for an openly available image-processing pipeline In MICCAI Workshop on Statistical Atlases and Cardiac Models of the Heart, Edited by T. Engstrom. Berlin: Springer, 2016.
"Refractoriness in human atria: Time and voltage dependence of sodium channel availability." Journal of Molecular and Cellular Cardiology 101 (2016): 26-34.
Action Potential Repolarisation in Healthy and Fibrillating Human Atria: Contribution of Small Conductance Calcium-Activated Potassium Channels In Conference of the Scandinavian Physiological Society., 2014.
"Investigations of the NaV\beta1b Sodium Channel Subunit in Human Ventricle; Functional Characterization of the H162P Brugada Syndrome Mutant." Heart and Circulatory Physiology 306 (2014): H1204-H1212.