PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging

Bobrov, Eugene; Georgievskaya, Anastasia; Kiselev, Konstantin; Sevastopolsky, Artem; Zhavoronkov, Alex; Gurov, Sergey; Рудаков, К. В.; Tobar, Maria del Pilar Bonilla; Jaspers, Sören; Clemann, Sven

doi:10.18632/aging.101629

Cited by 76 publications

(60 citation statements)

References 28 publications

(29 reference statements)

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“…Advanced machine learning tools are naturally called to improve the biological age predictions, see, e.g., a model trained from clinical blood markers (Putin et al 2016) and facial photos (Bobrov et al 2018). The examples presented here show that the full power of the deep learning architectures could be harnessed for feature extraction and non-linear models fitting of risks, rather than chronological age models (Pyrkov et al 2018b).…”

Section: Discussionmentioning

confidence: 99%

Biological age is a universal marker of aging, stress, and frailty

Pyrkov¹,

Федичев

2019

Preprint

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We carried out a systematic investigation of supervised learning techniques for biological age modeling. The biological aging acceleration is associated with the remaining healthand life-span. Artificial Deep Neural Networks (DNN) could be used to reduce the error of chronological age predictors, though often at the expense of the ability to distinguish health conditions. Mortality and morbidity hazards models based on survival follow-up data showed the best performance. Alternatively, logistic regression trained to identify chronic diseases was shown to be a good approximation of hazards models when data on survival follow-up times were unavailable. In all models, the biological aging acceleration was associated with disease burden in persons with diagnosed chronic age-related conditions. For healthy individuals, the same quantity was associated with molecular markers of inflammation (such as C-reactive protein), smoking, current physical, and mental health (including sleeping troubles, feeling tired or little interest in doing things). The biological age thus emerged as a universal biomarker of age, frailty and stress for applications involving large scale studies of the effects of longevity drugs on risks of diseases and quality of life.To be published as Chapter 2 in "Biomarkers of aging", ed. A. Moskalev, Springer, 2019. 2

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Section: Discussionmentioning

confidence: 99%

Biological age is a universal marker of aging, stress, and frailty

Pyrkov¹,

Федичев

2019

Preprint

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“…Indeed, since 2013, many aging clocks have been developed in both humans and other model organisms. The published aging clocks utilizing deep learning were developed using standard clinical blood tests 42 , facial images 43 , physical activity data, 44 and transcriptomic data 45 . These clocks were used to rank the most important features contributing to the accuracy of the prediction by using the permutation feature importance (PFI), deep feature selection (DFS) and other techniques.…”

Section: Introductionmentioning

confidence: 99%

Human microbiome aging clocks based on deep learning and tandem of permutation feature importance and accumulated local effects

Galkin

Aliper

Putin

et al. 2018

Preprint

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The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous analyses of gut microflora revealed associations between specific microbes and host health and disease status, genotype and diet. Here, we developed a method of predicting biological age of the host based on the microbiological profiles of gut microbiota using a curated dataset of 1,165 healthy individuals (3,663 microbiome samples). Our predictive model, a human microbiome clock, has an architecture of a deep neural network and achieves the accuracy of 3.94 years mean absolute error in cross-validation. The performance of the deep microbiome clock was also evaluated on several additional populations. We further introduce a platform for biological interpretation of individual microbial features used in age models, which relies on permutation feature importance and accumulated local effects. This approach has allowed us to define two lists of 95 intestinal biomarkers of human aging. We further show that this list can be reduced to 39 taxa that convey the most information on their host's aging. Overall, we show that (a) microbiological profiles can be used to predict human age; and (b) microbial features selected by models are age-related.

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“…Evolution of metrics of biological aging. First‐generation metrics of aging were based on one type of dataset (DNA methylation, [ 106 ] transcriptomic data, [ 86 ] facial images, [ 144 ] etc.) correlated with chronological age.…”

Section: Future Perspective Of Aging Assessment In Humanmentioning

confidence: 99%

Aging Biomarkers: From Functional Tests to Multi‐Omics Approaches

Kudryashova¹,

Burka²,

Kulaga

et al. 2020

Proteomics

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Aging results in various deleterious changes in the human body that may lead to loss of function and the manifestation of chronic diseases. While diseases can generally be reliably diagnosed, the aging process itself requires more sophisticated approaches to evaluate its progression. Numerous attempts have been made to establish biomarkers to quantify human aging at the cellular, tissue, and organismal level. Here, an up‐to‐date overview of biomarkers related to human aging with an emphasis on biomarkers that take into account different mechanisms of aging between individuals is provided. Classical discrete molecular and non‐molecular biomarkers handpicked by researches on the base of their strong correlation with age, as well as emerging omics‐based biomarkers, are discussed and potential future directions and developments in the field of aging assessment are outlined.

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PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging

Cited by 76 publications

References 28 publications

Biological age is a universal marker of aging, stress, and frailty

Biological age is a universal marker of aging, stress, and frailty

Human microbiome aging clocks based on deep learning and tandem of permutation feature importance and accumulated local effects

Aging Biomarkers: From Functional Tests to Multi‐Omics Approaches

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