Sam Gelman

Also affiliated with Morgridge Institute for Research

Biochemistry, Genetics and Molecular Biology

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 100%percentile

0Author DataRank

Indexed papers

0in pilot corpus

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Papers

Driven by 4 papers. Top paper: “Neural networks to learn protein sequence–function relationships from deep mutational scanning data”.

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

Proceedings of the National Academy of Sciences(2021)10.1073/pnas.2104878118

N/A

4.5DataRank · unranked

180 citations

Sarah A. Fahlberg, Pete Heinzelman, Philip A. Romero, Anthony Gitter, Sam Gelman

Biophysics-based protein language models for protein engineering

Nature Methods(2025)10.1038/s41592-025-02776-2

N/A

0.682DataRank · unranked

36 citations

Bryce Johnson, Chase R. Freschlin, Arnav Sharma, Sameer D’Costa, John Peters +3 more

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

bioRxiv (Cold Spring Harbor Laboratory)(2020)10.1101/2020.10.25.353946

N/A

0DataRank · unranked

17 citations

Sarah A. Fahlberg, Pete Heinzelman, Philip A. Romero, Anthony Gitter, Sam Gelman

Biophysics-based protein language models for protein engineering

bioRxiv (Cold Spring Harbor Laboratory)(2024)10.1101/2024.03.15.585128

N/A

0DataRank · unranked

29 citations

Bryce Johnson, Chase R. Freschlin, Arnav Sharma, Sameer D’Costa, John Peters +3 more