Author

Asa Gilmore

University of WashingtonORCID 0009-0009-1132-9155Unclaimed 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:benchmarking· 2 Organism:human· 2 Assay:imaging· 2 Approach:benchmark study· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Assay:multiome· 1

Frequent Co-Authors

Ariel RokemiD· 2 shared Aaron LeeiD· 1 shared Anita Esi Eshun· 1 shared Arel EnciniD· 1 shared Erin W. DickieiD· 1 shared Tristan GlatardiD· 1 shared Yue WuiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 12 mutual co-author links

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2 shared papersNeuroscience

Ariel RokemORCID

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2 papers

Recent·Most cited

A Comparative Evaluation of Structural MRI Foundation Models for Age, Sex, and Body-Mass Index Predictions

Arel Encin, Asa Gilmore, Ariel Rokem, Erin W. Dickie, Tristan Glatard

This study benchmarks four structural MRI foundation models for age, sex, and BMI prediction, finding that 3D-Neuro-SimCLR and AnatCL outperformed baselines overall.

bioRxiv · Sep 4, 2026 · PreprintNeuroscience

Vessels hiding in plain sight: quantifying brain vascular morphology in anatomical MR images using deep learning

Asa Gilmore, Anita Esi Eshun, Yue Wu, Aaron Lee, Ariel Rokem

The paper trains a deep learning model to quantify brain vascular morphology from standard MR images, showing it captures aging and health-related features not found in traditional metrics.

bioRxiv · May 6, 2025 · PreprintNeuroscience