Modeling transcriptomic age using knowledge-primed artificial neural networks Article Swipe
Nicholas Holzscheck
,
Cassandra Falckenhayn
,
Jörn Söhle
,
Boris Kristof
,
Ralf Siegner
,
André Werner
,
Janka Schössow
,
Clemens Jürgens
,
Henry Völzke
,
Horst Wenck
,
Marc Winnefeld
,
Elke Grönniger
,
Lars Kaderali
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1038/s41514-021-00068-5
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1038/s41514-021-00068-5
The development of ‘age clocks’, machine learning models predicting age from biological data, has been a major milestone in the search for reliable markers of biological age and has since become an invaluable tool in aging research. However, beyond their unquestionable utility, current clocks offer little insight into the molecular biological processes driving aging, and their inner workings often remain non-transparent. Here we propose a new type of age clock, one that couples predictivity with interpretability of the underlying biology, achieved through the incorporation of prior knowledge into the model design. The clock, an artificial neural network constructed according to well-described biological pathways, allows the prediction of age from gene expression data of skin tissue with high accuracy, while at the same time capturing and revealing aging states of the pathways driving the prediction. The model recapitulates known associations of aging gene knockdowns in simulation experiments and demonstrates its utility in deciphering the main pathways by which accelerated aging conditions such as Hutchinson–Gilford progeria syndrome, as well as pro-longevity interventions like caloric restriction, exert their effects.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41514-021-00068-5
- https://www.nature.com/articles/s41514-021-00068-5.pdf
- OA Status
- gold
- Cited By
- 64
- References
- 96
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3165645117
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3165645117Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41514-021-00068-5Digital Object Identifier
- Title
-
Modeling transcriptomic age using knowledge-primed artificial neural networksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-01Full publication date if available
- Authors
-
Nicholas Holzscheck, Cassandra Falckenhayn, Jörn Söhle, Boris Kristof, Ralf Siegner, André Werner, Janka Schössow, Clemens Jürgens, Henry Völzke, Horst Wenck, Marc Winnefeld, Elke Grönniger, Lars KaderaliList of authors in order
- Landing page
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https://doi.org/10.1038/s41514-021-00068-5Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41514-021-00068-5.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.nature.com/articles/s41514-021-00068-5.pdfDirect OA link when available
- Concepts
-
Interpretability, Progeria, Artificial neural network, Gene regulatory network, Artificial intelligence, Computer science, Longevity, Machine learning, Neuroscience, Biology, Computational biology, Gene, Gene expression, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
64Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 16, 2024: 19, 2023: 21, 2022: 7, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
96Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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