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Machine learning prediction of prime editing efficiency across diverse chromatin contexts.

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Title: Machine learning prediction of prime editing efficiency across diverse chromatin contexts.
Authors: Mathis, Nicolas1 (AUTHOR), Allam, Ahmed2 (AUTHOR), Tálas, András1 (AUTHOR), Kissling, Lucas1 (AUTHOR), Benvenuto, Elena1 (AUTHOR), Schmidheini, Lukas1,3 (AUTHOR), Schep, Ruben4,5 (AUTHOR), Damodharan, Tanav1 (AUTHOR), Balázs, Zsolt2 (AUTHOR), Janjuha, Sharan1 (AUTHOR), Ioannidi, Eleonora I.1 (AUTHOR), Böck, Desirée1 (AUTHOR), van Steensel, Bas4,5 (AUTHOR), Krauthammer, Michael2 (AUTHOR), Schwank, Gerald1 (AUTHOR) schwank@pharma.uzh.ch
Source: Nature Biotechnology. May2025, Vol. 43 Issue 5, p712-719. 8p.
Database: Environment Complete
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An: 185238170
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  Data: Machine learning prediction of prime editing efficiency across diverse chromatin contexts.
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  Data: <searchLink fieldCode="JN" term="%22Nature+Biotechnology%22">Nature Biotechnology</searchLink>. May2025, Vol. 43 Issue 5, p712-719. 8p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eih&AN=185238170
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        Value: 10.1038/s41587-024-02268-2
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        Text: English
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              Text: May2025
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