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Applications of machine learning for immunophenotypic measurable residual disease assessment in acute myeloid leukemia
Mocking, T. R., van de Loosdrecht, A. A., Cloos, J. & Bachas, C., 21 May 2025, In: HemaSphere. 9, 5, 8 p., e70138.Research output: Contribution to journal › Review article › Academic › peer-review
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CD34+CD38- leukemia stem cells predict clinical outcomes in acute myeloid leukemia patients treated non-intensively with hypomethylating agents: ACUTE MYELOID LEUKEMIA
Reuvekamp, T., Ngai, L. L., den Hartog, D., Carbaat-Ham, J., Fayed, M. M. H. E., Scholten, W. J., Mocking, T. R., Chitu, D. A., Pabst, T., Klein, S. K., Stussi, G., Griskevicius, L., Breems, D., van Lammeren-Venema, D., Boersma, R., Ossenkoppele, G. J., van de Loosdrecht, A. A., Bachas, C., Huls, G. A. & de Leeuw, D. C. & 1 others, , Apr 2025, In: Leukemia. 39, 4, p. 972-975 4 p.Research output: Contribution to journal › Article › Academic › peer-review
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Computational assessment of measurable residual disease in acute myeloid leukemia using mixture models
Mocking, T. R., Kelder, A., Reuvekamp, T., Ngai, L. L., Rutten, P., Gradowska, P., van de Loosdrecht, A. A., Cloos, J. & Bachas, C., 19 Dec 2024, In: Communications medicine. 4, 1, 271.Research output: Contribution to journal › Article › Academic › peer-review
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