APA (7th ed.) Citation

Mathis, N., Allam, A., Tálas, A., Kissling, L., Benvenuto, E., Schmidheini, L., . . . Schwank, G. (2025). Machine learning prediction of prime editing efficiency across diverse chromatin contexts. Nature Biotechnology: The Science and Business of Biotechnology, 43(5), 712. https://doi.org/10.1038/s41587-024-02268-2

Chicago Style (17th ed.) Citation

Mathis, Nicolas, et al. "Machine Learning Prediction of Prime Editing Efficiency Across Diverse Chromatin Contexts." Nature Biotechnology: The Science and Business of Biotechnology 43, no. 5 (2025): 712. https://doi.org/10.1038/s41587-024-02268-2.

MLA (9th ed.) Citation

Mathis, Nicolas, et al. "Machine Learning Prediction of Prime Editing Efficiency Across Diverse Chromatin Contexts." Nature Biotechnology: The Science and Business of Biotechnology, vol. 43, no. 5, 2025, p. 712, https://doi.org/10.1038/s41587-024-02268-2.