Trending With Impact: Machine Learning Predicts Human Aging

Will you age quickly or slowly? Is it possible to predict how long you will live based on your genetics, lifestyle and other traits? In a new study, a team of researchers—from the National Institutes of Health’s National Institute on Aging, University of California San Diego, University of Michigan, Consiglio Nazionale delle Ricerche, Azienda Sanitaria di Firenze, and ViQi, Inc.—sought to answer these questions by developing a novel framework designed to estimate human physiological age and aging rate. Their trending paper was published by Aging (Aging-US) in October 2021, and entitled, “Predicting physiological aging rates from a range of quantitative traits using machine learning”. “We present machine learning as a promising framework for measuring physiological age from broad-ranging physiological, cognitive, and molecular traits.” Full blog - https://www.mishablagosklonny.com/2021/11/05/trending-with-impact-machine-learning-predicts-human-aging/ Sign up for free Altmetric alerts about this article - https://oncotarget.altmetric.com/details/email_updates?id=10.18632%2Foncotarget.203660 DOI - https://doi.org/10.18632/aging.203660 Full Text - https://www.aging-us.com/article/203660/text Correspondence to: Luigi Ferrucci email: ferruccilu@grc.nia.nih.gov, David Schlessinger email: schlessingerd@grc.nia.nih.gov, Ilya Goldberg email: ilya@viqi.org and Jun Ding email: jun.ding@nih.gov Keywords: physiological aging rate, quantitative trait, machine learning, aging clock, mortality, personalized medicine About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at http://www.Aging-US.com​​ or connect with us on: SoundCloud - https://soundcloud.com/aging-us​ Facebook - https://www.facebook.com/AgingUS/ Twitter - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/agingus​ LinkedIn - https://www.linkedin.com/company/aging​ Pinterest - https://www.pinterest.com/AgingUS/ Aging-US is published by Impact Journals, LLC please visit http://www.ImpactJournals.com​​ or connect with @ImpactJrnls Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM