Abstract
Recent advances in deep learning (DL) and other machine learning methods (ML) and the availability of large population data sets annotated with age and other features led to the development of the many predictors of chronological age frequently referred to as the deep aging clocks (DACs). Many of these aging clocks were pioneered by Insilico Medicine and are available for testing online via www.young.ai, www.aging.ai, and other resources. Several of the DACs demonstrate biological relevance and can be used for a variety of applications ranging from aging and disease target identification, identifying the age-related features implicated in diseases, the inference of causal graphs, analyze the population-specificity of the data type and analyze the mechanism of drug response and many others. The generative adversarial networks (GANs) can be used to synthesize new data of patients of specified ages in large volume providing for novel target identification techniques, as well as for anonymization and patient privacy methods. In this chapter we provide a brief overview of the DACs, their advantages, and disadvantages as well as the commentary on the challenges in this emerging field.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aliper, A. et al. (2016) In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state. Aging (Albany. NY). 8, 2127–2152
Aliper, A. et al. (2017) Towards natural mimetics of metformin and rapamycin. Aging (Albany. NY). 9, 2245–2268
Aliper A et al (2016) Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data. Mol. Pharm. 13:2524–2530
Artemov, A. V et al. (2016) Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes. bioRxiv https://doi.org/10.1101/095653
Bakula, D. et al. (2018) Aging and drug discovery. Aging (Albany. NY). 10, 3079–3088
Bobrov, E. et al. (2018) PhotoAgeClock: deep learning algorithms for development of non-invasive visual biomarkers of aging. Aging (Albany. NY). 10, 3249–3259
Cole JH et al (2017) Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage 163:115–124
Galkin, F. et al. (2018) Human microbiome aging clocks based on deep learning and tandem of permutation feature importance and accumulated local effects. bioRxiv https://doi.org/10.1101/507780
Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol. 14:R115
Kadurin A et al (2017a) The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget 8:10883–10890
Kadurin A et al (2017b) druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico. Mol. Pharm. 14:3098–3104
LeCun Y et al (2015) Deep learning. Nature 521:436
Lee H et al (2017) Fully Automated Deep Learning System for Bone Age Assessment. J. Digit. Imaging 30:427–441
Mamoshina P et al (2016) Applications of Deep Learning in Biomedicine. Mol. Pharm. 13:1445–1454
Mamoshina P et al (2018a) Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations. Journals Gerontol. Ser. A 73:1482–1490
Mamoshina P et al (2018b) Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers. Sci. Rep, Accepted proof
Mamoshina P et al (2018c) Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification. Front. Genet. 9:242
Miotto R et al (2016) Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Sci. Rep. 6:26094
Polykovskiy D et al (2018) Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery. Mol. Pharm. 15:4398–4405
Putin E et al (2016) Deep biomarkers of human aging: Application of deep neural networks to biomarker development. 8:1–13
Putin E et al (2018a) Adversarial Threshold Neural Computer for Molecular de Novo Design. Mol. Pharm. 15:4386–4397
Putin E et al (2018b) Reinforced Adversarial Neural Computer for de Novo Molecular Design. J. Chem. Inf. Model. 58:1194–1204
Pyrkov TV et al (2018) Extracting biological age from biomedical data via deep learning: too much of a good thing? Sci. Rep. 8:5210
Song, J. et al. (2017) Dual Conditional GANs for Face Aging and Rejuvenation
Zhavoronkov A et al (2014) Signaling pathway cloud regulation for in silico screening and ranking of the potential geroprotective drugs. Front. Genet. 5:49
Zhavoronkov A (2018) Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry. Mol. Pharm. 15:4311–4313
Zhavoronkov A et al (2019) Artificial intelligence for aging and longevity research: Recent advances and perspectives. Ageing Res. Rev. 49:49–66
Acknowledgements
The authors would like to thank Nvidia Corporation and personally Mark Berger for providing early access to the graphics processing units (GPUs) that helped power the development of the DACs.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
AZ and PM work for Insilico Medicine, a for-profit longevity biotechnology company developing the end-to-end target identification and drug discovery pipeline for a broad spectrum of age-related diseases. The company applied for multiple patents covering the various methods for development and applications of the aging clocks. The company may have commercial interests in this publication.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Mamoshina, P., Zhavoronkov, A. (2019). Deep Integrated Biomarkers of Aging. In: Moskalev, A. (eds) Biomarkers of Human Aging. Healthy Ageing and Longevity, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-24970-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-24970-0_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24969-4
Online ISBN: 978-3-030-24970-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)