Over the past decade, epigenetic clocks have emerged as powerful machine learning tools,
not only to estimate chronological and biological age but also to assess the efficacy of anti …

Alzheimer's disease (AD) is associated with heterogeneous atrophy patterns. We employed
a semi-supervised representation learning technique known as Surreal-GAN, through which …

The complex biological mechanisms underlying human brain aging remain incompletely
understood. This study investigated the genetic architecture of three brain age gaps (BAG) …

Abstract Machine learning has been increasingly used to obtain individualized
neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in …

An approach to age modeling involves the supervised prediction of age using machine
learning from subject features. The derived age metrics are used to study the relationship …

Biological age is an indicator of whether an individual is experiencing rapid, slowing, or
normal aging. Perceived age is highly correlated with biological age, which reflects health …

Disease heterogeneity poses a significant challenge for precision diagnostics. Recent work
leveraging artificial intelligence has offered promise to dissect this heterogeneity by …

Aging, the predominant risk factor for numerous diseases, manifests in various forms across
the structure and architecture of tissues of the human body, offering the opportunity to …

Multi-organ biological aging clocks derived from clinical phenotypes and neuroimaging
have emerged as valuable tools for studying human aging and disease1, 2, 3, 4. Plasma …

Artificial intelligence (AI) has been increasingly integrated into imaging genetics to provide
intermediate phenotypes (ie, endophenotypes) that bridge the genetics and clinical …