| SurvSHAP(t): Time-dependent explanations of machine learning survival models M Krzyziński, M Spytek, H Baniecki, P Biecek Knowledge-Based Systems 262, 110234, 2023 | 33 | 2023 |
| survex: an R package for explaining machine learning survival models M Spytek, M Krzyziński, SH Langbein, H Baniecki, MN Wright, P Biecek Bioinformatics 39 (12), btad723, 2023 | 1 | 2023 |
| Challenges facing the explainability of age prediction models: case study for two modalities M Spytek, W Hryniewska-Guzik, J Zygierewicz, J Rogala, P Biecek arXiv preprint arXiv:2303.06640, 2023 | 1 | 2023 |
| Interpretable Machine Learning for Survival Analysis SH Langbein, M Krzyziński, M Spytek, H Baniecki, P Biecek, MN Wright arXiv preprint arXiv:2403.10250, 2024 | | 2024 |
| Antibody selection strategies and their impact in predicting clinical malaria based on multi-sera data A Fonseca, M Spytek, P Biecek, C Cordeiro, N Sepúlveda BioData Mining 17 (1), 2, 2024 | | 2024 |
| Ki67 is a better marker than PRAME in risk stratification of BAP1-positive and BAP1-loss uveal melanomas P Donizy, M Spytek, M Krzyziński, K Kotowski, A Markiewicz, ... British Journal of Ophthalmology, 2023 | | 2023 |
| Topic analysis performed on data from Reddit M Kurek, MI Kędzierska, MJ Spytek Zakład Sztucznej Inteligencji i Metod Obliczeniowych, 2023 | | 2023 |
| Antibody selection strategies and their impact in the analysis of malaria multi-sera data A Fonseca, M Spytek, P Biecek, C Cordeiro, N Sepúlveda medRxiv, 2022.10. 06.22280719, 2022 | | 2022 |
| survex: model-agnostic explainability for survival analysis M Spytek, M Krzyziński, H Baniecki, P Biecek | | |
