Author

Sam Gelman

University of Wisconsin–MadisonORCID 0000-0001-9537-0976Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:protein sequence· 2 Research Focus:protein design· 1 Research Focus:sequence-to-function modeling· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

Anthony GitteriD· 2 shared Philip A. RomeroiD· 2 shared Arnav SharmaiD· 1 shared Bryce JohnsoniD· 1 shared Chase R. FreschliniD· 1 shared John Peters· 1 shared Pete HeinzelmaniD· 1 shared Sameer D’CostaiD· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

9 top collaborators · 26 mutual co-author links

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2 shared papersBioinformatics

Anthony GitterORCID

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2 papers

Recent·Most cited

BioinformaticsNature MethodsPeer-reviewedSep 1, 2025

Biophysics-based protein language models for protein engineering

Sam Gelman, Bryce Johnson, Chase R. Freschlin…·Philip A. Romero

This paper introduces METL, a protein language model that integrates biophysical modeling with machine learning to predict protein properties and design functional variants.

70 citationstop 1%

protein sequencedeep learning

𝕏 PostBluesky

BioinformaticsProceedings of the National Academy of SciencesPeer-reviewedNov 23, 2021

Neural networks to learn protein sequence-function relationships from deep mutational scanning data

Sam Gelman, Sarah A. Fahlberg, Pete Heinzelman…·Anthony Gitter

This paper develops a deep learning framework to predict protein function from sequence using deep mutational scanning data and demonstrates its ability to design new proteins with improved properties.

164 citationstop 1%

protein sequencedeep learning

𝕏 PostBluesky