 (6) The hyperdimensional transform for distributional modelling, regression and classificationP. Dewulf, B. De Baets and M. Stock(2025) NEURAL COMPUTING AND APPLICATIONS. 37, 19393-19422. |
| (5) The hyperdimensional transform: a holographic representation of functionsP. Dewulf, M. Stock and B. De Baets(2025) IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING. 19, 3-18. |
| (4) Machine-learning-based prediction of disability progression in multiple sclerosis: an observational, international, multi-center studyE. De Brouwer, T. Becker, L. Werthen-Brabants, P. Dewulf, D. Iliadis, C. Dekeyser, G. Lure's, B. Van Wijmeersch, V. Popescu, T. Dhaene, D. Deschrijver, W. Waegeman, B. De Baets, M. Stock, D. Horakova, E.K. Havrdova, S. Ozakbas, F. Pattio, G. Izquierdo, S. Eichau, A. Prat, M. Girard, M. Onofrj, A. Lugaresi, P. Grammond, T. Kalincik, R. Alroughani, Y. Moreau and L. Peeters(2024) PLOS DIGITAL HEALTH. 3, e0000533. |
| (3) Hyperdimensional computing: a fast, robust and interpretable paradigm for biological dataM. Stock, W. Van Criekinge, D. Boeckaerts, P. Dewulf, S. Taelman, M. Van Haeverbeke and B. De Baets(2024) PLOS COMPUTATIONAL BIOLOGY. 20, e1012426 . |
| (2) Link prediction in stagewise graphsP. Dewulf, M. Stock and B. De Baets(2024) IEEE TRANSACTIONS ON DATA AND KNOWLEDGE ENGINEERING. 36, 3252-3264. |
| (1) Cold-start problems in data-driven prediction of drug-drug interaction effectsP. Dewulf, M. Stock and B. De Baets(2021) PHARMACEUTICALS. 14, 429. |