APA (7th ed.) Citation

Elgart, M., Lyons, G., Romero-Brufau, S., Kurniansyah, N., Brody, J. A., Guo, X., . . . Sofer, T. (2022). Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations. Communications biology, 5(1), 856-12. https://doi.org/10.1038/s42003-022-03812-z

Chicago Style (17th ed.) Citation

Elgart, Michael, et al. "Non-linear Machine Learning Models Incorporating SNPs and PRS Improve Polygenic Prediction in Diverse Human Populations." Communications Biology 5, no. 1 (2022): 856-12. https://doi.org/10.1038/s42003-022-03812-z.

MLA (9th ed.) Citation

Elgart, Michael, et al. "Non-linear Machine Learning Models Incorporating SNPs and PRS Improve Polygenic Prediction in Diverse Human Populations." Communications Biology, vol. 5, no. 1, 2022, pp. 856-12, https://doi.org/10.1038/s42003-022-03812-z.