Author

Fee Schmidt

Universitätsklinikum Tübingen

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Bioinformatics· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:cell type annotation· 1 Assay:CITE-seq· 1 Research Focus:clustering· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Frederic Pohl· 1 shared Frederik GrawiD· 1 shared Georg Andreas SchettiD· 1 shared Goerkem OsmanustaiD· 1 shared Guido H. WabnitziD· 1 shared Hanns‐Martin LorenziD· 1 shared Jörg HenesiD· 1 shared Nicolaj S. HackertiD· 1 shared

Activity by Year

2024-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

1 shared paperBioinformatics

Frederic Pohl

Mutual co-authors in cluster

Author profile →

1 paper

Recent·Most cited

Accurate, fast and memory efficient quantification of immune cell phenotypes in cytometry using machine learning

Tarik Exner, Nicolaj S. Hackert, Frederic Pohl, Goerkem Osmanusta, Fee Schmidt, Hanns‐Martin Lorenz, Guido H. Wabnitz, Georg Andreas Schett, Frederik Graw, Jörg Henes, Ricardo Grieshaber‐Bouyer

The paper benchmarks machine learning algorithms for cytometry data analysis, finding decision trees provide high accuracy, speed, and memory efficiency for immune cell phenotype quantification.

bioRxiv · Jul 26, 2024 · PreprintBioinformatics