Machine Learning Designed for Any Hematologic Flow Cytometry Data Set
diagnFlow provides a valuable data set-agnostic approach to flow cytometry data sets previously not leveraged for automatic analysis while maintaining interpretability and resource efficiency.
Authors
- Katharina Kriegsmann (ORCID: https://orcid.org/0000-0002-3620-1491)
- Carsten Müller‐Tidow (ORCID: https://orcid.org/0000-0002-7166-5232)
- Nora Liebers
- Sascha Dietrich (ORCID: https://orcid.org/0000-0002-0648-1832)
- Calin-Petru Manta
- Tobias Roider (ORCID: https://orcid.org/0000-0002-6973-3531)
- Sarah Richter (ORCID: https://orcid.org/0000-0002-4906-0499)
- Michael Hundemer (ORCID: https://orcid.org/0000-0002-5596-2405)
- Felix Czernilofsky (ORCID: https://orcid.org/0000-0001-5868-2890)
- Johannes Mammen (ORCID: https://orcid.org/0000-0002-0749-4360)
Institutions
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- Düsseldorf University Hospital (DE)
- National Center for Tumor Diseases (DE)
- European Molecular Biology Laboratory (DE)
- Heinrich Heine University Düsseldorf (DE)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1200/cci-24-00259
- Citations
- 1
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.81