Author

G. Slabaugh

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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Bioengineering· 1 Cell Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:deep learning· 2 Assay:imaging· 2 Research Focus:clustering· 1 Research Focus:drug response / drug discovery· 1 Organism:mouse· 1 Research Focus:perturbation response prediction· 1 Assay:proteomics· 1

Frequent Co-Authors

A. Lobley· 1 shared C. A. Schurman· 1 shared I. Sequeira· 1 shared S. Dreyer Vetter· 1 shared S. W. Verbruggen· 1 shared T. Alliston· 1 shared W. Dee· 1 shared

Activity by Year

2023-2023 · peak 2/yr

Co-Author Collaboration Network

7 top collaborators · 9 mutual co-author links

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1 shared paperCell Biology

A. Lobley

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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

Cell-Vision Fusion: A Swin Transformer-based Approach to Predicting Kinase Inhibitor Mechanism of Action from Cell Painting Data

W. Dee, I. Sequeira, A. Lobley, G. Slabaugh

This paper develops a Swin Transformer-based model to predict kinase inhibitor mechanisms of action from Cell Painting data, achieving higher accuracy than traditional image-based profile methods.

bioRxiv · Dec 13, 2023Cell Biology