Aging is a complex and multifactorial process that increases the risk of various age-related
diseases and there are many aging clocks that can accurately predict chronological age …

Target discovery is crucial for the development of innovative therapeutics and diagnostics.
However, current approaches often face limitations in efficiency, specificity, and scalability …

Aging biology is a promising and burgeoning research area that can yield dual-purpose
pathways and protein targets that may impact multiple diseases, while retarding or possibly …

First published in 2016, predictors of chronological and biological age developed using
deep learning (DL) are rapidly gaining popularity in the aging research community. These …

Aging is a significant contributor to various chronic diseases, thus emerging as an
increasingly important area of research. Elucidating the mechanisms underlying aging can …

The development of anti-aging interventions requires quantitative measurement of biological
age. Machine learning models, known as “aging clocks,” are built by leveraging diverse …

In our recent transcriptomic meta-analysis, we used random forest machine learning to
accurately predict age in human blood, bone, brain, heart, and retina tissues given gene …

Accurate and physiologically meaningful biomarkers for human aging are key to assessing
antiaging therapies. Given ethnic differences in health, diet, lifestyle, behavior …

One of the major impediments in human aging research is the absence of a comprehensive
and actionable set of biomarkers that may be targeted and measured to track the …

It is hypothesized that there are inter-individual differences in biological aging; however,
differences in aging among (heart images vs. electrophysiology) and across (eg, brain vs …