An interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging.

MLA

Melendez, Justin, et al. “An Interpretable Machine Learning-Based Cerebrospinal Fluid Proteomics Clock for Predicting Age Reveals Novel Insights into Brain Aging.” Aging Cell, vol. 23, no. 9, Sept. 2024, p. e14230. EBSCOhost, https://doi.org/10.1111/acel.14230.

APA

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(9), e14230. https://doi.org/10.1111/acel.14230

Chicago

Melendez, Justin, Yun Ju Sung, Miranda Orr, Andrew Yoo, Suzanne Schindler, Carlos Cruchaga, and Randall Bateman. 2024. “An Interpretable Machine Learning-Based Cerebrospinal Fluid Proteomics Clock for Predicting Age Reveals Novel Insights into Brain Aging.” Aging Cell 23 (9): e14230. doi:10.1111/acel.14230.