Researchers at the Hebrew University have developed an exceptionally accurate method for predicting chronological age from DNA, based on two short genomic regions. Using deep learning networks analyzing DNA methylation patterns at a single-molecule resolution, they achieve age predictions with a median error as low as 1.36 years in individuals under 50. The method is unaffected by smoking, BMI, and sex, and has potential applications in forensics, aging research, and personalized medicine.

A team of researchers at the Hebrew University of Jerusalem, led by Bracha Ochana and Daniel Nudelman, under the supervision of Prof. Tommy Kaplan, Prof. Yuval Dor and Prof. Ruth Shemer, has developed a remarkably precise method to estimate a person’s age based on just a small DNA sample—offering breakthrough potential for medicine, aging research, and forensic investigations.

Using cutting-edge artificial intelligence, the scientists created a tool called MAgeNet that can determine a person’s chronological age—the number of years since birth—with a margin of error as small as 1.36 years for individuals under 50. And all it takes is a simple blood draw.

“It turns out that the passage of time leaves measurable marks on our DNA,” said Prof. Kaplan. “Our model decodes those marks with astonishing precision.”

The secret lies in how our DNA changes over time through a process called methylation—the chemical “tagging” of DNA by methyl group (CH₃) . By zooming in on just two key regions of the human genome, the team was able to read these changes at the level of individual molecules, then use deep learning to translate them into accurate age predictions.

The study, published in Cell Reports, analyzed blood samples from over 300 healthy people, as well as data from a decade-long longitudinal analysis of the Jerusalem Perinatal Study (JPS), led by Prof. Hagit Hochner from the Faculty of Medicine. As they show, the model worked consistently across a range of variables—like smoking, body weight, sex, and even different signs of biological aging.

Beyond potential medical uses, the method could also revolutionize forensic science by allowing experts to estimate a suspect’s age from just a trace of DNA—something existing tools struggle to do.

“This gives us a new window into how aging works at the cellular level,” said Prof. Dor. “It’s a powerful example of what happens when biology meets AI.”
The research also uncovered new patterns in how DNA changes over time, suggesting our cells encode age both randomly and in coordinated bursts—like ticking biological clocks. “It’s not just about knowing your age,” added Prof. Shemer. “It’s about understanding how your cells keeps track of time, molecule by molecule.”

Why it matters: This research could reshape how we approach health, aging, and identity in the future. From helping doctors tailor treatments based on a person's true biological timeline to giving forensic investigators a powerful new tool for solving crimes, the ability to read age directly from DNA opens the door to breakthroughs across science, medicine, and law. It also deepens our understanding of how aging works—bringing us one step closer to decoding the body's internal clock.