Author

Mads Eggert Nielsen

University of CopenhagenORCID 0000-0001-6170-8836Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:cell signaling & trafficking· 2 Assay:proteomics· 2 Organism:Arabidopsis· 1 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Approach:genetic perturbation / CRISPR· 1

Frequent Co-Authors

David Havndrup MunchiD· 2 shared Benjamin L. KochiD· 1 shared Brian D. RutteriD· 1 shared Dillon GardneriD· 1 shared H. A. SmithiD· 1 shared Hans Thordal‐ChristenseniD· 1 shared Hector Molinelli RubiatoiD· 1 shared Jakob Nybo NisseniD· 1 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersPlant Biology

David Havndrup MunchORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

Plant BiologybioRxivPreprintNov 12, 2026

RabA- and RabE-mediated trafficking define distinct branches of plant pre-invasive immunity

Mads Eggert Nielsen, Jelco Mensch, David Havndrup Munch, Hector Molinelli Rubiato

The paper identifies RabE and RabA-mediated trafficking as distinct pathways in plant pre-invasive immunity, with RabE specific to powdery mildew and RabA to oomycete pathogens.

DOI 10.64898/2026.11.10.765595

plant (other)proteomics

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Plant BiologyPLANT PHYSIOLOGYPeer-reviewedFeb 1, 2026

Molecular Insights into the Production of Extracellular Vesicles by Plants

Benjamin L. Koch, Dillon Gardner, H. A. Smith…·Roger W. Innes

This study identifies proteins and pathways involved in plant EV secretion, revealing roles for EXO70, RIN4, and VAP27 in EV heterogeneity and plant immunity.

12 citationstop 1%

Arabidopsisproteomics

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BioinformaticsNature CommunicationsPeer-reviewedSep 27, 2024

Taxometer: Improving taxonomic classification of metagenomics contigs

Svetlana Kutuzova, Mads Eggert Nielsen, Pau Piera Líndez…·Simon Rasmussen

Taxometer improves taxonomic classification of metagenomics contigs by combining abundance profiles and tetra-nucleotide frequencies, enhancing accuracy across multiple tools.

23 citations

metagenomicsdeep learning

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