Machine learning model identifies gene target that extends mouse lifespan

Researchers have used artificial intelligence to discover a gene target that can reverse signs of biological aging in animals without damaging normal cell function. According to a study published in Nature Aging, scientists built a computational model trained on human cell samples to evaluate transcriptomic aging patterns. The system identified a gene named SRSF1, which helps regulate how cells process genetic information.

When researchers activated this gene in elderly mice, the animals experienced a 14 percent increase in median lifespan and a 44 percent reduction in physical frailty. Traditional cellular reprogramming methods often carry severe risks, such as triggering tumors by forcing cells to lose their specialized functions. In contrast, the study showed that targeting SRSF1 restored youthful qualities to mouse tissues while preserving fundamental cell identity.

Although these results highlight how computer models can speed up drug discovery for healthy aging, translating rodent findings to human patients remains a major hurdle. It is still unclear whether activating this gene in humans over many years might cause hidden side effects. A key question is whether AI systems can accurately forecast how whole human organ systems, rather than isolated cells in a lab dish, will respond to anti aging therapies long term.