A 17-gene stemness score for rapid determination of risk in acute leukaemiaStanley Ng, Jean Wang, Lars Bullinger et al.|Nature|2016Cited by 916
Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventionsKirsten Zantvoort, Burkhardt Funk, Barbara Nacke et al.|npj Digital Medicine|2024Cited by 68
Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learningJan‐Niklas Eckardt, Jan Moritz Middeke, Christoph Röllig et al.|Haematologica|2022Cited by 47
Differential impact of <i>IDH1</i>/<i>2</i> mutational subclasses on outcome in adult AML: results from a large multicenter studyJan Moritz Middeke, Christian Thiede, Klaus H. Metzeler et al.|Blood Advances|2021Cited by 44
Secondary-type mutations do not impact outcome in NPM1-mutated acute myeloid leukemia – implications for the European LeukemiaNet risk classificationJan‐Niklas Eckardt, Christoph Röllig, Marius Bill et al.|Leukemia|2023Cited by 36