Bekkers, Erik J.

Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias

Kolk, M. Z. H., Ruipérez-Campillo, S., Allaart, C. P., Wilde, A. A. M., Knops, R. E., Narayan, S. M., Tjong, F. V. Y., DEEP RISK investigators, Raijmakers, F. D., Van Der Lingen, A.-L. C. J., et al. (2024) · Scientific Reports

Cardiac electrophysiology and arrhythmiasECG Monitoring and Analysis

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CKConv: Continuous Kernel Convolution For Sequential Data

Romero, D. W., Kuzina, A., Bekkers, E. J., Tomczak, J. M., & Hoogendoorn, M. (2021) · arXiv

Neural Networks and ApplicationsAdvanced Neural Network Applications

Cited by 39

Improving generalizability and data efficiency for MHC-I binding peptide predictions through structure-based geometric deep learning

Xue, L., Marzella, D., Crocioni, G., Radusinović, T., Lepikhov, D., Severin, H., Bodor, D., Rademaker, D., Lin, C., Georgievska, S., et al. (2024) · Communications Biology

vaccines and immunoinformatics approachesMachine Learning in Bioinformatics

Cited by 18