Author

Jörg Henes

Universitätsklinikum TübingenORCID 0000-0002-8385-6861Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Organism:human· 2 Assay:CITE-seq· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Approach:machine learning (classical)· 1 Assay:spatial transcriptomics· 1

Frequent Co-Authors

Georg Andreas SchettiD· 2 shared Alexander KreuteriD· 1 shared Alexandru MicuiD· 1 shared Alexandru‐Emil MateiiD· 1 shared Andrea‐Hermina GyörfiiD· 1 shared Ann‐Christin PecheriD· 1 shared Bernhard HomeyiD· 1 shared Christina BergmanniD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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2 shared papersImmunology

Georg Andreas SchettORCID

Mutual co-authors in cluster

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2 papers

Recent·Most cited

ImmunologyAnnals of the Rheumatic DiseasesPeer-reviewedJul 2, 2025

Spatially informed phenotyping by cyclic-in-situ-hybridization identifies novel fibroblast populations and their pathogenic niches in systemic sclerosis

Yinan Li, Tim Filla, Andrea‐Hermina Györfi…·Alexandru‐Emil Matei

The paper uses spatial transcriptomic methods to identify novel fibroblast subpopulations in systemic sclerosis skin and their pathogenic roles.

11 citationstop 3%

humanspatial transcriptomics

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BioinformaticsbioRxivPreprintJul 26, 2024

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

Tarik Exner, Nicolaj S. Hackert, Frederic Pohl…·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.

DOI 10.1101/2024.07.26.605341

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