Deep learning models incorporating endogenous factors beyond DNA sequences improve the prediction accuracy of base editing outcomes.

MLA

Yuan, Tanglong, et al. “Deep Learning Models Incorporating Endogenous Factors beyond DNA Sequences Improve the Prediction Accuracy of Base Editing Outcomes.” Cell Discovery, vol. 10, no. 1, Feb. 2024, p. 20. EBSCOhost, https://doi.org/10.1038/s41421-023-00624-1.

APA

Yuan, T., Wu, L., Li, S., Zheng, J., Li, N., Xiao, X., Zhang, H., Fei, T., Xie, L., Zuo, Z., Li, D., Huang, P., Feng, H., Cao, Y., Yan, N., Wei, X., Shi, L., Sun, Y., Wei, W., … Zuo, E. (2024). Deep learning models incorporating endogenous factors beyond DNA sequences improve the prediction accuracy of base editing outcomes. Cell Discovery, 10(1), 20. https://doi.org/10.1038/s41421-023-00624-1

Chicago

Yuan, Tanglong, Leilei Wu, Shiyan Li, Jitan Zheng, Nana Li, Xiao Xiao, Haihang Zhang, et al. 2024. “Deep Learning Models Incorporating Endogenous Factors beyond DNA Sequences Improve the Prediction Accuracy of Base Editing Outcomes.” Cell Discovery 10 (1): 20. doi:10.1038/s41421-023-00624-1.