Author

F. Buytenhuijs

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

Biology Disciplines

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Immunology· 2

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Assay:CITE-seq· 1 Approach:deep learning· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1

Frequent Co-Authors

J. Textor· 2 shared S. Sultan· 2 shared A. Fähnrich· 1 shared C. G. Figdor· 1 shared D. Rogers· 1 shared E. Mallet Gauthier· 1 shared E. Martynova· 1 shared H. Melichar· 1 shared

Activity by Year

2021-2022 · peak 1/yr

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10 top collaborators · 30 mutual co-author links

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2 shared papersImmunology

J. Textor

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2 papers

Recent·Most cited

ImmunologybioRxivNov 23, 2022

Machine learning analysis of the T cell receptor repertoire identifies sequence features that predict self-reactivity

J. Textor, F. Buytenhuijs, D. Rogers…·J. N. Mandl

The paper uses machine learning to identify TCR sequence features predicting self-reactivity, finding that n-nucleotide additions and acidic amino acids reduce self-reactivity.

DOI 10.1016/j.cels.2023.11.004

mouseCITE-seq

𝕏 PostBluesky

ImmunologybioRxivOct 22, 2021

ImmuNet: A Segmentation-Free Machine Learning Pipeline for Immune Landscape Phenotyping in Tumors by Muliplex Imaging

S. Sultan, M. A. J. Gorris, E. Martynova…·J. Textor

ImmuNet is a machine learning pipeline that identifies cell positions and phenotypes in multiplex imaging without segmentation, outperforming segmentation-based methods with error rates below 5-10%.

DOI 10.1093/biomethods/bpae094

imagingdeep learning

𝕏 PostBluesky