By combining transcriptomic data with other data sources, inferences can be made about
functional changes during ageing. Thus, we conducted a meta-analysis on 127 publicly …

Abstract Deep Neural Networks (DNN) have been recently developed for the estimation of
Biological Age (BA), the hypothetical underlying age of an organism, which can differ from its …

As the global population becomes older, understanding the impact of aging on health and
disease becomes paramount. Recent advancements in multiomic technology have allowed …

Single-cell multiomics technologies typically measure multiple types of molecule from the
same individual cell, enabling more profound biological insight than can be inferred by …

Aging biomarkers are a combination of biological parameters to (i) assess age-related
changes,(ii) track the physiological aging process, and (iii) predict the transition into a …

Contemporary longitudinal studies collect repeated measurements of biomarkers allowing
one to analyze their dynamics in relation to mortality, morbidity, or other health-related …

Aging is characterized by a progressive deterioration of physiological integrity, leading to
impaired functional ability and ultimately increased susceptibility to death. It is a major risk …

Data on biological mechanisms of aging are mostly obtained from cross-sectional study
designs. An inherent disadvantage of this design is that inter-individual differences can …

Biological age (BA), derived from molecular and physiological measurements, has been
proposed to better predict mortality and disease than chronological age (CA). In the present …

Biological aging of human organ systems reflects the interplay of age, chronic disease,
lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes …