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

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.

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Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest FG, PM, AA, AZ work for Insilico Medicine, a for-profit biotechnology company developing the end-to-end target identification and drug discovery pipeline for a broad spectrum of age-related diseases. The company may have commercial interests in this publication. Products of InSilico Medicine include “Young.AI” system mentioned in this article. PM works for Deep Longevity, a for-profit longevity company. JPM is an advisor for Centaura, Longevity Vision Fund and is the founder of Magellan Science Ltd, a company providing consulting services in longevity science.