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Results: 3
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  • Open Access
    Papapanagiotou, I., Bumbuc, R. V., Korkmaz, H. I., Krzhizhanovskaya, V., & Sheraton, V. M. (2025). From simulations to surrogates: Neural networks enhancing burn wound healing predictions. Journal of Computational Science, 89, Article 102593. https://doi.org/10.1016/j.jocs.2025.102593
  • Open Access
    Korkmaz, H. I., Sheraton, V. M., Bumbuc, R. V., Li, M., Pijpe, A., Mulder, P. P. G., Boekema, B. K. H. L., de Jong, E., Papendorp, S. G. F., Brands, R., Middelkoop, E., Sloot, P. M. A., & van Zuijlen, P. P. M. (2024). An in silico modeling approach to understanding the dynamics of the post-burn immune response. Frontiers in Immunology, 15, Article 1303776. https://doi.org/10.3389/fimmu.2024.1303776
  • Bumbuc, R. V., Korkmaz, H. I., van Zuijlen, P. P. M., Vermeulen, L., & Sheraton, V. M. (2023). Understanding the Dynamics of the Proliferative Phase in Local Burn Wound Healing: A Computational Model. In X. Jiang, H. Wang, R. Alhajj, X. Hu, F. Engel, M. Mahmud, N. Pisanti, X. Cui, & H. Song (Eds.), Proceedings : 2023 IEEE International Conference on Bioinformatics and Biomedicine: December 5-8, 2023, Istanbul & Turkey (pp. 3676-3683). IEEE. https://doi.org/10.1109/BIBM58861.2023.10385875
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