Quantitative comparison of microarray experiments with published leukemia related gene expression signatures. - Hans-Ulrich Klein; Christian Ruckert; Alexander Kohlmann; Lars Bullinger; Christian Thiede; Torsten Haferlach; Martin Dugas (BMC Bioinform., 2009)

Finding small somatic structural variants in exome sequencing data: a machine learning approach. - Matthias Kuhn; Thoralf Stange; Sylvia Herold; Christian Thiede; Ingo Roeder (Comput. Stat., 2018)

Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology. - Katja Hoffmann; Katja Cazemier; Christoph Baldow; Silvio Schuster; Yuri Kheifetz; Sibylle Schirm; Matthias Horn; Thomas Ernst 006; Constanze Volgmann; Christian Thiede; Andreas Hochhaus; Martin Bornhäuser; et al. (BMC Medical Informatics Decis. Mak., 2020)

Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence. - Jan-Niklas Eckardt; Waldemar Hahn; Christoph Röllig; Sebastian Stasik; Uwe Platzbecker; Carsten Müller-Tidow; Hubert Serve; Claudia D. Baldus; Christoph Schliemann; Kerstin Schäfer-Eckart; Maher Hanoun; Martin Kaufmann; et al. (npj Digit. Medicine, 2024)

Synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification models. - Jan-Niklas Eckardt; Ishan Srivastava; Zizhe Wang; Susann Winter; Tim Schmittmann; Sebastian Riechert; Miriam Eva Helena Gediga; Anas Shekh Sulaiman; Martin M. K. Schneider; Freya Schulze; Christian Thiede; Katja Sockel; et al. (npj Digit. Medicine, 2025)