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Aging

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

Overview of attention for article published in Aging, May 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#2 of 2,842)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Citations

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164 Dimensions

Readers on

mendeley
310 Mendeley
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1 CiteULike
Title
Deep biomarkers of human aging: Application of deep neural networks to biomarker development
Published in
Aging, May 2016
DOI 10.18632/aging.100968
Pubmed ID
Authors

Evgeny Putin, Polina Mamoshina, Alexander Aliper, Mikhail Korzinkin, Alexey Moskalev, Alexey Kolosov, Alexander Ostrovskiy, Charles Cantor, Jan Vijg, Alex Zhavoronkov

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.

Twitter Demographics

The data shown below were collected from the profiles of 49 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 310 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Russia 2 <1%
Canada 1 <1%
United Kingdom 1 <1%
Brazil 1 <1%
Japan 1 <1%
United States 1 <1%
Unknown 303 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 65 21%
Researcher 65 21%
Student > Master 39 13%
Student > Bachelor 28 9%
Other 26 8%
Other 55 18%
Unknown 32 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 68 22%
Agricultural and Biological Sciences 51 16%
Computer Science 42 14%
Medicine and Dentistry 26 8%
Engineering 19 6%
Other 59 19%
Unknown 45 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 782. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 July 2021.
All research outputs
#14,197
of 18,760,506 outputs
Outputs from Aging
#2
of 2,842 outputs
Outputs of similar age
#360
of 274,684 outputs
Outputs of similar age from Aging
#1
of 35 outputs
Altmetric has tracked 18,760,506 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,842 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 274,684 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.