Author

M. Brehler

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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Cell Biology· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Organism:C. elegans· 1 Research Focus:cell signaling & trafficking· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Organism:human· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

A. K. Lowman· 1 shared A. Nencka· 1 shared A. Winiarz· 1 shared C. Conze· 1 shared C. Meyer-Schwesinger· 1 shared D. Loreth· 1 shared F. Kyereme· 1 shared J. Brand· 1 shared

Activity by Year

2022-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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1 shared paperPathology

A. K. Lowman

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Author profile →

2 papers

Recent·Most cited

Podocyte exopher-formation as a novel pathomechanism in membranous nephropathy

K. Lahme, W. Sachs, S. Froembling, M. Brehler, D. Loreth, K. Surmann, S. Gaffling, U. Wedekind, V. Boettcher-Dierks, M. R. Adler, P. J. Saez, C. Conze, R. Thuenauer, S. Skuza, K. Neitzel, , C. Meyer-Schwesinger

This study identifies podocyte exopher-formation as a novel mechanism for removing antigen/autoantibody aggregates in membranous nephropathy, with potential for non-invasive monitoring.

bioRxiv · Apr 4, 2024 · PreprintCell Biology

Comparison of machine learning to deep learning for automated annotation of Gleason patterns in whole mount prostate cancer histology.

S. R. Duenweg, M. Brehler, S. A. Bobholz, A. K. Lowman, A. Winiarz, F. Kyereme, A. Nencka, K. A. Iczkowski, P. LaViolette

This study compares machine learning and deep learning methods for Gleason pattern annotation in prostate cancer histology, finding that deep learning better differentiates Gleason patterns.

bioRxiv · Nov 10, 2022 · PreprintPathology