Machine Learning May Predict How Well Youll Age

first_img Machine Learning Estimates Risk of Cardiovascular DeathMachine Learning Translates Japanese Retro Games In Real Time An algorithm may one day be able to anticipate your chances of aging well.Researchers at the Salk Institute analyzed skin cells of the very young to the very old, searching for molecular signatures that can be predictive of age.“This experiment was designed to determine whether there are molecular signatures of aging across the entire range of the human lifespan,” co-senior author Saket Navlakha, an assistant professor in Salk’s Integrative Biology Laboratory, said in a statement.“We want to develop algorithms that can predict healthy aging and non-healthy aging, and try to find the differences,” he said.Developing a better understanding of the biological processes of aging could help address elderly health conditions like heart disease and dementia.The team collected samples of dermal fibroblasts—a type of skin cell that generate connective tissue and help skin heal after injury—from 133 healthy people ranging in age from one to 94.They also used fibroblasts from 10 patients with genetic disease progeria, characterized by accelerated aging.A human fibroblast cell line was derived from a skin biopsy (via Roberta Schulte/Swati Tyagi/Salk Institute)Using RNA sequencing and custom machine-learning algorithms, scientists found certain biomarkers indicating aging, and were able to predict a person’s age with less than eight years error, on average.“We took a ‘kitchen sink’ approach with this project,” first author Jason Fleischer, a Salk postdoctoral fellow, said.“Rather than going into this research with an idea of what we wanted to find, we decided to look at the changes in expression of all the protein-coding genes and let the algorithms sort it out,” he explained. “We used what’s called an ensemble machine-learning method to do this.”If validated, the Salk team’s findings could be adopted by doctors to better screen patients for age-related conditions, and advise them about healthy lifestyle choices.“Aging is a driver of so many diseases, including Alzheimer’s and other neurologic problems,” Navlakha said. “If we are able to show that the changes we’ve seen in fibroblasts are connected with aging in other types of cells, we may eventually be able to use these signatures to develop targeted interventions.”Additional research is required before preventative treatments could be developed.The full study was published this month in the journal Genome Biology.More on Designs AI-Enabled Fingernail Sensor to Track DiseasesSuperglue-Style Hydrogel Could Help Eliminate Joint PainFirst Baby Born Via Uterus Transplant From Deceased Donor Stay on targetlast_img read more

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