Gene expression, the process by which the body manufactures the protein components of molecular machinery and control mechanisms, changes constantly. Some proteins are turned out in greater quantities, some less. This should be an ongoing record of the changes that happen with aging, and therefore also helpful on the path to slowing, preventing, and reversing aging. If we can make any sense of the complexity, that is: "To study how gene changes are related to individual longevity, we need another type of data in addition to gene expression profiles: the survival time of individual animals after their gene expression is measured. With this information, we could determine which transcriptional responses are associated with a longer lifespan, and in principle even develop a personalized medicine approach to aging: we could train a machine learning algorithm to peek at the expression levels of a handful of crucial genes and predict your physiological age - and the number of healthy years you have left. Previous [studies] of aging humans haven't included survival times because we live too long. Recently, some human survival data - together with matching gene expression data from lymphoblastoid cell lines - have become available from a long-range study that began in the early 1980s. In the first aging study to take advantage of this resource, [researchers] mine the data to identify gene changes associated with longevity."