Unsupervised learning of aging principles from longitudinal data

Abstract

Age is the leading risk factor for prevalent diseases and death. However, the relation between age-related physiological changes and lifespan is poorly understood. We combined analytical and machine learning tools to describe the aging process in large sets of longitudinal measurements. Assuming that aging results from a dynamic instability of the organism state, we designed a deep artificial neural network, including auto-encoder and auto-regression (AR) components. The AR model tied the dynamics of physiological state with the stochastic evolution of a single variable, the “dynamic frailty indicator” (dFI). In a subset of blood tests from the Mouse Phenome Database, dFI increased exponentially and predicted the remaining lifespan. The observation of the limiting dFI was consistent with the late-life mortality deceleration. dFI changed along with hallmarks of aging, including frailty index, molecular markers of inflammation, senescent cell accumulation, and responded to life-shortening (high-fat diet) and life-extending (rapamycin) treatments.

Physics-informed machine learning and its structural integrity applications: state of the art

“…The rapid development of machine learning (ML) has led to the widespread application of datadriven methods in both science and industry. Owing to its powerful function approximation capability and big data analysis ability, ML provides potential solutions for solving problems where the physical mechanism is not fully understood [1][2][3][4][5][6] and improving the computational efficiency of numerical simulations [7][8][9][10][11][12]. To guarantee the structural integrity and performance requirements of industrial equipment and infrastructures under complex working conditions, several efforts has been made to apply ML to failure mechanism modelling and PHM.…”