Journal
Aging cell
Publication Date
9-1-2024
Volume
23
Issue
9
First Page
e14230
Document Type
Open Access Publication
DOI
10.1111/acel.14230
Rights and Permissions
Melendez, J., Sung, Y. J., Orr, M., Yoo, A., Schindler, S., Cruchaga, C., & Bateman, R. (2024). An interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging. Aging Cell, 23, e14230. https://doi.org/10.1111/acel.14230 © 2024 The Author(s). Aging Cell published by Anatomical Society and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium
Recommended Citation
Melendez, Justin; Sung, Yun Ju; Orr, Miranda; Yoo, Andrew; Schindler, Suzanne; Cruchaga, Carlos; and Bateman, Randall, "An interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging." Aging cell. 23, 9. e14230 (2024).
https://digitalcommons.wustl.edu/oa_4/5808
Department
ICTS (Institute of Clinical and Translational Sciences)
Additional Links
Supplemental material is available for this article at publisher site.
