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Aging (Albany NY). 2016 May;8(5):1021-33. doi: 10.18632/aging.100968.

Deep biomarkers of human aging: Application of deep neural networks to biomarker development.

Author information

1
Pharma.AI Department, Insilico Medicine, Inc, Baltimore, MD 21218, USA.
2
Computer Technologies Lab, ITMO University, St. Petersburg 197101, Russia.
3
The Biogerontology Research Foundation, Oxford, UK.
4
School of Systems Biology, George Mason University (GMU), Fairfax, VA 22030, USA.
5
Invitro Laboratory, Ltd, Moscow 125047, Russia.
6
Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.
7
Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.

Abstract

One of the major impediments in human aging research is the absence of a comprehensive and actionable set of biomarkers that may be targeted and measured to track the effectiveness of therapeutic interventions. In this study, we designed a modular ensemble of 21 deep neural networks (DNNs) of varying depth, structure and optimization to predict human chronological age using a basic blood test. To train the DNNs, we used over 60,000 samples from common blood biochemistry and cell count tests from routine health exams performed by a single laboratory and linked to chronological age and sex. The best performing DNN in the ensemble demonstrated 81.5 % epsilon-accuracy r = 0.90 with R(2) = 0.80 and MAE = 6.07 years in predicting chronological age within a 10 year frame, while the entire ensemble achieved 83.5% epsilon-accuracy r = 0.91 with R(2) = 0.82 and MAE = 5.55 years. The ensemble also identified the 5 most important markers for predicting human chronological age: albumin, glucose, alkaline phosphatase, urea and erythrocytes. To allow for public testing and evaluate real-life performance of the predictor, we developed an online system available at http://www.aging.ai. The ensemble approach may facilitate integration of multi-modal data linked to chronological age and sex that may lead to simple, minimally invasive, and affordable methods of tracking integrated biomarkers of aging in humans and performing cross-species feature importance analysis.

KEYWORDS:

aging biomarkers; biomarker development; deep learning; deep neural networks; human aging; machine learning

Comment in

PMID:
27191382
PMCID:
PMC4931851
DOI:
10.18632/aging.100968
[Indexed for MEDLINE]
Free PMC Article

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