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May 8, 2016

Deep biomarkers of human aging: Application of deep neural networks to biomarker development. Aging (Albany NY).

Written and published by Biogerontology Research Foundation staff in collaboration with Albert Einstein College of Medicine (Department of Genetics), Boston University (Department of Biomedical Engineering), George Mason University (School of Systems Biology), Insilico Medicine, Inc. (Pharma.AI Department), ITMO University (Computer Technologies Lab), and Invitro Laboratory, Ltd.

Deep biomarkers of human aging: Application of deep neural networks to biomarker development. Aging (Albany NY). 2016 May;8(5):1021-33.

Putin E(1,2), Mamoshina P(1,3), Aliper A(1), Korzinkin M(1), Moskalev A(1,4), Kolosov A(5), Ostrovskiy A(5), Cantor C(6), Vijg J(7), Zhavoronkov A(1,3).

(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.

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