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

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Bibliographic Details
Title: Machine learning prediction of prime editing efficiency across diverse chromatin contexts.
Authors: Mathis N; Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland., Allam A; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland., Tálas A; Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland., Kissling L; Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland., Benvenuto E; Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland., Schmidheini L; Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.; Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland., Schep R; Oncode Institute, Netherlands Cancer Institute, Amsterdam, the Netherlands.; Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, the Netherlands., Damodharan T; Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland., Balázs Z; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland., Janjuha S; Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland., Ioannidi EI; Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland., Böck D; Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland., van Steensel B; Oncode Institute, Netherlands Cancer Institute, Amsterdam, the Netherlands.; Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, the Netherlands., Krauthammer M; Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland., Schwank G; Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland. schwank@pharma.uzh.ch.
Source: Nature biotechnology [Nat Biotechnol] 2025 May; Vol. 43 (5), pp. 712-719. Date of Electronic Publication: 2024 Jun 21.
Publication Type: Journal Article
Journal Info: Publisher: Nature America Publishing Country of Publication: United States NLM ID: 9604648 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1546-1696 (Electronic) Linking ISSN: 10870156 NLM ISO Abbreviation: Nat Biotechnol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1546-1696
DOI:10.1038/s41587-024-02268-2