Interpretable machine learning for high-dimensional trajectories of aging health.

MLA

Farrell, Spencer, et al. “Interpretable Machine Learning for High-Dimensional Trajectories of Aging Health.” PLoS Computational Biology, vol. 18, no. 1, Jan. 2022, pp. 1–30. EBSCOhost, https://doi.org/10.1371/journal.pcbi.1009746.

APA

Farrell, S., Mitnitski, A., Rockwood, K., & Rutenberg, A. D. (2022). Interpretable machine learning for high-dimensional trajectories of aging health. PLoS Computational Biology, 18(1), 1–30. https://doi.org/10.1371/journal.pcbi.1009746

Chicago

Farrell, Spencer, Arnold Mitnitski, Kenneth Rockwood, and Andrew D. Rutenberg. 2022. “Interpretable Machine Learning for High-Dimensional Trajectories of Aging Health.” PLoS Computational Biology 18 (1): 1–30. doi:10.1371/journal.pcbi.1009746.