Author

Jake Galvin

Johns Hopkins UniversityPrimary ORCID profile (0000-0002-8219-0156 · 1 paper)Unclaimed ORCID profile · follow to get notified of new preprints

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Jake GalviniD1 paper · Johns Hopkins University

Biology Disciplines

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Organism:human· 1 Assay:proteomics· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1

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Alexis J. BattleiD· 1 shared Eduarda Vaz· 1 shared Lena Wang· 1 shared Rebecca KeeneriD· 1 shared

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2025-2025 · peak 1/yr

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4 top collaborators · 6 mutual co-author links

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1 shared paperGenetics

Alexis J. BattleORCID

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1 paper

Recent·Most cited

Fine-tuning sequence to function deep learning models on large-scale proteomic data improves the accuracy of variant effect prediction

Eduarda Vaz, Lena Wang, Jake Galvin, Rebecca Keener, Alexis J. Battle

The paper fine-tunes a sequence-to-function model on large proteomic data to improve variant effect prediction, showing better performance with larger sample sizes and rare variants.

bioRxiv · Sep 26, 2025 · PreprintGenetics