Biologically informed deep learning for explainable epigenetic clocks.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
15 Jan 2024
15 Jan 2024
Historique:
received:
17
01
2023
accepted:
20
12
2023
medline:
16
1
2024
pubmed:
16
1
2024
entrez:
15
1
2024
Statut:
epublish
Résumé
Ageing is often characterised by progressive accumulation of damage, and it is one of the most important risk factors for chronic disease development. Epigenetic mechanisms including DNA methylation could functionally contribute to organismal aging, however the key functions and biological processes may govern ageing are still not understood. Although age predictors called epigenetic clocks can accurately estimate the biological age of an individual based on cellular DNA methylation, their models have limited ability to explain the prediction algorithm behind and underlying key biological processes controlling ageing. Here we present XAI-AGE, a biologically informed, explainable deep neural network model for accurate biological age prediction across multiple tissue types. We show that XAI-AGE outperforms the first-generation age predictors and achieves similar results to deep learning-based models, while opening up the possibility to infer biologically meaningful insights of the activity of pathways and other abstract biological processes directly from the model.
Identifiants
pubmed: 38225268
doi: 10.1038/s41598-023-50495-5
pii: 10.1038/s41598-023-50495-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1306Subventions
Organisme : European Union's Horizon 2020
ID : 101021607
Organisme : National Research, Development and Innovation Office
ID : K128780
Organisme : Nemzeti Kutatási Fejlesztési és Innovációs Hivatal
ID : NKFIH-OTKA-FK142835
Organisme : European Union project
ID : RRF-2.3.1-21-2022-00004
Informations de copyright
© 2024. The Author(s).
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