Aging.AI specifications


Unique identifier OMICS_13957
Name Aging.AI
Interface Web user interface
Restrictions to use None
Input data Blood tests data
Output data Predicted age and sex
Computer skills Basic
Version 2.0
Stability Stable
Maintained Yes


  • person_outline Alex Zhavoronkov <>

Publication for Aging.AI

Aging.AI in publications

PMCID: 5310653
PMID: 28132958
DOI: 10.18632/aging.101163

[…] with 83.5% epsilon-accuracy r of 0.91 with r(2) of 0.82 and mean absolute error (mae) of 5.55 years., following on the footsteps of microsoft's, we built a website inviting people from all over the world to submit their test results in order to guess their age. from january 15 to march 31st over 1,500 people participated in the study. in addition […]

PMCID: 5076461
PMID: 27622833
DOI: 10.18632/aging.101034

[…] basic biomarkers (albumin, glucose, alkaline phosphatase, urea, erythrocytes, cholesterol, rdw, alpha-2 globulins, hematocrit, and lymphocytes) in the online tool provided by putin et al. [] at alpha-2-globulins were only present in inchianti, so we left the field empty in the other data sets (the dnn is capable of treating missing data, though this reduces accuracy). in addition, […]

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Aging.AI institution(s)
Pharma.AI Department, Insilico Medicine, Inc, Baltimore, MD, USA; Computer Technologies Lab, ITMO University, St. Petersburg, Russia; The Biogerontology Research Foundation, Oxford, UK; School of Systems Biology, George Mason University (GMU), Fairfax, VA, USA; Invitro Laboratory, Ltd, Moscow, Russia; Department of Biomedical Engineering, Boston University, Boston, MA, USA; Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
Aging.AI funding source(s)
This work was financially supported by the Government of Russian Federation, Grant 074-U01 and by a research grant from the Life Extension Foundation 2016-LEF- AA-INSIL.

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