Publication: Predicting CRISPR-Cas9 Prime Editing Efficiency across Diverse Edits and Chromatin Contexts with Machine Learning
Predicting CRISPR-Cas9 Prime Editing Efficiency across Diverse Edits and Chromatin Contexts with Machine Learning
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Mathis, N. (2024). Predicting CRISPR-Cas9 Prime Editing Efficiency across Diverse Edits and Chromatin Contexts with Machine Learning. (Dissertation, University of Zurich) https://doi.org/10.5167/uzh-259612
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Genome editing offers the unprecedented ability to precisely modify genetic material, signaling a new era in our approach to understanding life and treating genetic diseases. Recent advances in genome editing technologies have greatly enhanced their applicability. Among these technologies, prime editing stands out for its ability to precisely edit the genome while minimizing the occurrence of double-strand breaks typically associated with conventional CRISPR-Cas9 editing. This thesis focuses on improving the editing efficiency of prim
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Mathis, N. (2024). Predicting CRISPR-Cas9 Prime Editing Efficiency across Diverse Edits and Chromatin Contexts with Machine Learning. (Dissertation, University of Zurich) https://doi.org/10.5167/uzh-259612
