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

Galkin, Fedor, Mamoshina, Polina, Aliper, Alex, de Magalhaes, Joao Pedro ORCID: 0000-0002-6363-2465, Gladyshev, Vadim N and Zhavoronkov, Alex (2020) Biohorology and biomarkers of aging: Current state-of-the-art, challenges and opportunities. AGEING RESEARCH REVIEWS, 60. 101050-.

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Official URL: http://dx.doi.org/10.1016/j.arr.2020.101050

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Aging, Biogerontology, Aging clock, Deep learning, Neural network
Depositing User:	Symplectic Admin
Date Deposited:	28 Apr 2020 09:44
Last Modified:	18 Jan 2023 23:53
DOI:	10.1016/j.arr.2020.101050
Open Access URL:	https://www.sciencedirect.com/science/article/pii/...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3084677