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Predicting physiological aging rates from a range of quantitative traits using machine learning.

Aging

Sun ED, Qian Y, Oppong R, Butler TJ, Zhao J, Chen BH, Tanaka T, Kang J, Sidore C, Cucca F, Bandinelli S, Abecasis GR, Gorospe M, Ferrucci L, Schlessinger D, Goldberg I, Ding J.
PMID: 34718232
Aging (Albany NY). 2021 Oct 29;13(20):23471-23516. doi: 10.18632/aging.203660. Epub 2021 Oct 29.

It is widely thought that individuals age at different rates. A method that measures "physiological age" or physiological aging rate independent of chronological age could therefore help elucidate mechanisms of aging and inform an individual's risk of morbidity and...

Cite
Sun ED, Qian Y, Oppong R, et al. Predicting physiological aging rates from a range of quantitative traits using machine learning. Aging (Albany NY). 2021;13(20):23471-23516doi: 10.18632/aging.203660.
Sun, E. D., Qian, Y., Oppong, R., Butler, T. J., Zhao, J., Chen, B. H., Tanaka, T., Kang, J., Sidore, C., Cucca, F., Bandinelli, S., Abecasis, G. R., Gorospe, M., Ferrucci, L., Schlessinger, D., Goldberg, I., & Ding, J. (2021). Predicting physiological aging rates from a range of quantitative traits using machine learning. Aging, 13(20), 23471-23516. https://doi.org/10.18632/aging.203660
Sun, Eric D, et al. "Predicting physiological aging rates from a range of quantitative traits using machine learning." Aging vol. 13,20 (2021): 23471-23516. doi: https://doi.org/10.18632/aging.203660
Sun ED, Qian Y, Oppong R, Butler TJ, Zhao J, Chen BH, Tanaka T, Kang J, Sidore C, Cucca F, Bandinelli S, Abecasis GR, Gorospe M, Ferrucci L, Schlessinger D, Goldberg I, Ding J. Predicting physiological aging rates from a range of quantitative traits using machine learning. Aging (Albany NY). 2021 Oct 29;13(20):23471-23516. doi: 10.18632/aging.203660. Epub 2021 Oct 29. PMID: 34718232; PMCID: PMC8580337.
Copy Download .nbib Format: NLM
Predicting physiological aging rates from a range of quantitative traits using machine learning.

Aging

Sun ED, Qian Y, Oppong R, Butler TJ, Zhao J, Chen BH, Tanaka T, Kang J, Sidore C, Cucca F, Bandinelli S, Abecasis GR, Gorospe M, Ferrucci L, Schlessinger D, Goldberg I, Ding J.
PMID: 34718232
Aging (Albany NY). 2021 Oct 29;13(20):23471-23516. doi: 10.18632/aging.203660. Epub 2021 Oct 29.

It is widely thought that individuals age at different rates. A method that measures "physiological age" or physiological aging rate independent of chronological age could therefore help elucidate mechanisms of aging and inform an individual's risk of morbidity and...

Cite
Sun ED, Qian Y, Oppong R, et al. Predicting physiological aging rates from a range of quantitative traits using machine learning. Aging (Albany NY). 2021;13(20):23471-23516doi: 10.18632/aging.203660.
Sun, E. D., Qian, Y., Oppong, R., Butler, T. J., Zhao, J., Chen, B. H., Tanaka, T., Kang, J., Sidore, C., Cucca, F., Bandinelli, S., Abecasis, G. R., Gorospe, M., Ferrucci, L., Schlessinger, D., Goldberg, I., & Ding, J. (2021). Predicting physiological aging rates from a range of quantitative traits using machine learning. Aging, 13(20), 23471-23516. https://doi.org/10.18632/aging.203660
Sun, Eric D, et al. "Predicting physiological aging rates from a range of quantitative traits using machine learning." Aging vol. 13,20 (2021): 23471-23516. doi: https://doi.org/10.18632/aging.203660
Sun ED, Qian Y, Oppong R, Butler TJ, Zhao J, Chen BH, Tanaka T, Kang J, Sidore C, Cucca F, Bandinelli S, Abecasis GR, Gorospe M, Ferrucci L, Schlessinger D, Goldberg I, Ding J. Predicting physiological aging rates from a range of quantitative traits using machine learning. Aging (Albany NY). 2021 Oct 29;13(20):23471-23516. doi: 10.18632/aging.203660. Epub 2021 Oct 29. PMID: 34718232; PMCID: PMC8580337.
Copy Download .nbib Format: NLM
Predicting physiological aging rates from a range of quantitative traits using machine learning.

Aging

Sun ED, Qian Y, Oppong R, Butler TJ, Zhao J, Chen BH, Tanaka T, Kang J, Sidore C, Cucca F, Bandinelli S, Abecasis GR, Gorospe M, Ferrucci L, Schlessinger D, Goldberg I, Ding J.
PMID: 34718232
Aging (Albany NY). 2021 Oct 29;13(20):23471-23516. doi: 10.18632/aging.203660. Epub 2021 Oct 29.

It is widely thought that individuals age at different rates. A method that measures "physiological age" or physiological aging rate independent of chronological age could therefore help elucidate mechanisms of aging and inform an individual's risk of morbidity and...

Cite
Sun ED, Qian Y, Oppong R, et al. Predicting physiological aging rates from a range of quantitative traits using machine learning. Aging (Albany NY). 2021;13(20):23471-23516doi: 10.18632/aging.203660.
Sun, E. D., Qian, Y., Oppong, R., Butler, T. J., Zhao, J., Chen, B. H., Tanaka, T., Kang, J., Sidore, C., Cucca, F., Bandinelli, S., Abecasis, G. R., Gorospe, M., Ferrucci, L., Schlessinger, D., Goldberg, I., & Ding, J. (2021). Predicting physiological aging rates from a range of quantitative traits using machine learning. Aging, 13(20), 23471-23516. https://doi.org/10.18632/aging.203660
Sun, Eric D, et al. "Predicting physiological aging rates from a range of quantitative traits using machine learning." Aging vol. 13,20 (2021): 23471-23516. doi: https://doi.org/10.18632/aging.203660
Sun ED, Qian Y, Oppong R, Butler TJ, Zhao J, Chen BH, Tanaka T, Kang J, Sidore C, Cucca F, Bandinelli S, Abecasis GR, Gorospe M, Ferrucci L, Schlessinger D, Goldberg I, Ding J. Predicting physiological aging rates from a range of quantitative traits using machine learning. Aging (Albany NY). 2021 Oct 29;13(20):23471-23516. doi: 10.18632/aging.203660. Epub 2021 Oct 29. PMID: 34718232; PMCID: PMC8580337.
Copy Download .nbib Format: NLM
Showing 1 to 3 of 3 entries