Author

D. Hess

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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Biochemistry· 1 Cell Biology· 1 Ecology· 1 Plant Biology· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 3 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Organism:human· 2 Assay:proteomics· 2 Assay:bulk RNA-seq· 1 Organism:C. elegans· 1 Approach:deep learning· 1

Frequent Co-Authors

A. Holzhausen· 1 shared C. Gentili· 1 shared C. Koenig· 1 shared C. Soneson· 1 shared D. Gaidatzis· 1 shared G. Kempf· 1 shared H. Grosshans· 1 shared J. Seebacher· 1 shared

Activity by Year

2019-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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1 shared paperPlant Biology

A. Holzhausen

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4 papers

Recent·Most cited

Massively parallel quantification of substrate turnover defines protease subsite cooperativity

R. K. Gudipati, D. Gaidatzis, J. Seebacher, S. Muehlhaeusser, G. Kempf, S. Cavadini, D. Hess, C. Soneson, H. Grosshans

The paper develops qPISA to quantify protease substrate turnover, revealing subsite cooperativity in DPP4 and distinguishing it from DPF-3.

bioRxiv · Nov 17, 2023 · PreprintBiochemistry

Insight into the central and nodal cells transcriptome of the streptophyte green alga Chara braunii S276

D. Hess, A. Holzhausen, W. R. Hess

The paper generates and compares RNA-seq data from nodal cells and whole plantlets of Chara braunii to identify transcriptomic differences related to development and function.

bioRxiv · Feb 12, 2023 · PreprintPlant Biology

The Human Exonuclease-1 Interactome And Phosphorylation Sites

W. Eid, D. Hess, C. Koenig, C. Gentili, S. Ferrari

This study identifies EXO1 interacting proteins and phosphorylation sites, revealing roles in DNA repair and potential cancer targets.

bioRxiv · Mar 26, 2019Cell Biology

Prediction of Algal Blooms in the Great Lakes through a Convolution Neural Network of Remote Sensing Data

K. Srinivasan, V. Duvvur, D. Hess

The paper trains a CNN on remote sensing data to predict algal bloom types in the Great Lakes with 80% accuracy using 900 images.

bioRxiv · Jan 29, 2019 · PreprintEcology