Author

C. A. Schurman

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 Bioengineering· 1

Signature Assays, Organisms & Methods

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Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Organism:human· 1 Assay:imaging· 1 Organism:mouse· 1 Research Focus:protein folding & dynamics· 1

Frequent Co-Authors

T. Alliston· 2 shared A. O'Broin· 1 shared B. Schilling· 1 shared C. D. King· 1 shared G. Slabaugh· 1 shared J. B. Burton· 1 shared J. Bons· 1 shared J. P. Rose· 1 shared

Activity by Year

2022-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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2 shared papersBioengineering

T. Alliston

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2 papers

Recent·Most cited

Deep learning models to map osteocyte networks can successfully distinguish between young and aged bone

S. Dreyer Vetter, C. A. Schurman, T. Alliston, G. Slabaugh, S. W. Verbruggen

This study uses deep learning to segment osteocyte networks and shows it can distinguish between young and aged mouse bones.

bioRxiv · Dec 20, 2023Bioengineering

Deep Coverage and Quantification of the Bone Proteome Provides Enhanced Opportunities for New Discoveries in Skeletal Biology and Disease

J. P. Rose, C. A. Schurman, C. D. King, J. Bons, J. B. Burton, S. K. Patel, A. O'Broin, T. Alliston, B. Schilling

This paper introduces a novel mass spectrometric workflow for deep bone proteome coverage and quantification, identifying key proteins and post-translational modifications relevant to bone aging and disease.

bioRxiv · Nov 20, 2022 · PreprintBiochemistry