Biohorology and biomarkers of aging: Current state-of-the-art, challenges and opportunities

[Display omitted] Telomere length, gene expression, blood chemical parameters and DNA-methylation status all undergo age-associated changes, which can be measured and used to predict chronological age with a varying error rate. This review focuses on technical approaches used to construct aging clocks and compares their efficiency. [Display omitted] •Biohorology is the science of measuring the passage of time in living systems.•Today there are dozens of aging clocks based on such biomarkers of aging as DNAm, gene expression and metabolomic profiles.•DNAm clocks are the most popular so far, but they have a number of frequently overlooked technical drawbacks.•Deep learning methods can be used to develop aging clocks using data types previously deemed too complicated.•Deep learning methods can also be used to extend aging clocks’ functionality beyond age prediction. The aging process results in multiple traceable footprints, which can be quantified and used to estimate an organism's age. Examples of such aging biomarkers include epigenetic changes, telomere attrition, and alterations in gene expression and metabolite concentrations. More than a dozen aging clocks use molecular features to predict an organism's age, each of them utilizing different data types and training procedures. Here, we offer a detailed comparison of existing mouse and human aging clocks, discuss their technological limitations and the underlying machine learning algorithms. We also discuss promising future directions of research in biohorology — the science of measuring the passage of time in living systems. Overall, we expect deep learning, deep neural networks and generative approaches to be the next power tools in this timely and actively developing field.