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Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning

Gemma May, Christina Akirtava, Matthew Agar-Johnson, Jelena Micic, John Woolford, Joel McManus
doi: https://doi.org/10.1101/2021.04.16.440232
Gemma May
1Department of Biological Sciences
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Christina Akirtava
1Department of Biological Sciences
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Matthew Agar-Johnson
1Department of Biological Sciences
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Jelena Micic
1Department of Biological Sciences
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John Woolford
1Department of Biological Sciences
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Joel McManus
1Department of Biological Sciences
2Computational Biology Department Carnegie Mellon University, Pittsburgh PA, USA
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  • For correspondence: mcmanus@andrew.cmu.edu
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Posted April 17, 2021.
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Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning
Gemma May, Christina Akirtava, Matthew Agar-Johnson, Jelena Micic, John Woolford, Joel McManus
bioRxiv 2021.04.16.440232; doi: https://doi.org/10.1101/2021.04.16.440232
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Unraveling the influences of sequence and position on yeast uORF activity using massively parallel reporter systems and machine learning
Gemma May, Christina Akirtava, Matthew Agar-Johnson, Jelena Micic, John Woolford, Joel McManus
bioRxiv 2021.04.16.440232; doi: https://doi.org/10.1101/2021.04.16.440232

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