<p>Machine learning-powered tools to elucidate species-specific promoter design rules across the Proteobacteria</p>

Layla A Schuster; Christopher R Reisch; Catalina Mejia; Lidimarie Trujillo Rodriguez; Emily Kairalla; Marc G Chevrette; Raquel Dias

doi:10.7490/f1000research.1119935.1

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Schuster LA, Reisch CR, Mejia C et al. Machine learning-powered tools to elucidate species-specific promoter design rules across the Proteobacteria [version 1; not peer reviewed]. F1000Research 2024, 13:1185 (poster) (https://doi.org/10.7490/f1000research.1119935.1)

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Machine learning-powered tools to elucidate species-specific promoter design rules across the Proteobacteria

Layla A Schuster, Christopher R Reisch, Catalina Mejia, Lidimarie Trujillo Rodriguez, Emily Kairalla, Marc G Chevrette¹, Raquel Dias¹

Published 09 Oct 2024 (https://doi.org/10.7490/f1000research.1119935.1)

Author Affiliations

¹ Microbiology and Cell Science, University of Florida Institute of Food and Agricultural Sciences, Gainesville, Florida, USA

Copyright © 2024 Schuster LA et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 09 Oct 2024

Machine learning-powered tools to elucidate species-specific promoter design rules across the Proteobacteria
[version 1; not peer reviewed]

Layla A Schuster, Christopher R Reisch, Catalina Mejia, Lidimarie Trujillo Rodriguez, Emily Kairalla, Marc G Chevrette¹, Raquel Dias¹

Author Affiliations

¹ Microbiology and Cell Science, University of Florida Institute of Food and Agricultural Sciences, Gainesville, Florida, USA

Presented at

Intelligent Systems for Molecular Biology Conference (ISMB), 2025

Abstract

Competing Interests

No competing interests were disclosed

Keywords

promoter, Proteobacteria, synthetic biology, machine learning, species-specific

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