bioRxiv
Neural networks to learn protein sequence-function relationships from deep mutational scanning data
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, Preprint
Gelman, S; Fahlberg, S; Heinzelman, P; Romero, P; Gitter, A
2020
Duke Scholars
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Gelman, S., Fahlberg, S., Heinzelman, P., Romero, P., & Gitter, A. (2020). Neural networks to learn protein sequence-function relationships from deep mutational scanning data. bioRxiv. https://doi.org/10.1101/2020.10.25.353946
Gelman, Sam, Sarah Fahlberg, Pete Heinzelman, Philip Romero, and Anthony Gitter. “Neural networks to learn protein sequence-function relationships from deep mutational scanning data.” BioRxiv, 2020. https://doi.org/10.1101/2020.10.25.353946.
Gelman S, Fahlberg S, Heinzelman P, Romero P, Gitter A. Neural networks to learn protein sequence-function relationships from deep mutational scanning data. bioRxiv. 2020.
Gelman, Sam, et al. “Neural networks to learn protein sequence-function relationships from deep mutational scanning data.” BioRxiv, 2020. Epmc, doi:10.1101/2020.10.25.353946.
Gelman S, Fahlberg S, Heinzelman P, Romero P, Gitter A. Neural networks to learn protein sequence-function relationships from deep mutational scanning data. bioRxiv. 2020.