Researchers have made significant strides in understanding aging through a machine learning approach that studies fruit flies and humans. Traditionally, findings impacting lifespan and healthspan are explored in fruit flies, then tested in mice, which is a costly and time-consuming process. However, scientists at the Buck Institute have developed a novel method that streamlines this research.
This innovative technique effectively bypasses the conventional step of using mice to evaluate discoveries relevant to humans. By employing machine learning algorithms, the study harnesses vast amounts of genetic and biological data to uncover patterns and insights that pertain to the aging process.