In recent years there has been a widespread interest in researching biomarkers of aging that could predict physiological vulnerability better than chronological age. Aging, in fact, is one of the most relevant risk factors for a wide range of maladies, and molecular surrogates of this phenotype could enable better patients stratification. Among the most promising of such biomarkers is DNA methylation-based biological age. Given the potential and variety of computational implementations (epigenetic clocks), we here present a systematic review of such clocks. Furthermore, we provide a large-scale performance comparison across different tissues and diseases in terms of age prediction accuracy and age acceleration, a measure of deviance from physiology. Our analysis offers both a state-of-the-art overview of the computational techniques developed so far and a heterogeneous picture of performances, which can be helpful in orienting future research.

Di Lena, P., Sala, C., Nardini, C. (2022). Evaluation of different computational methods for DNA methylation-based biological age. BRIEFINGS IN BIOINFORMATICS, 23(4), 1-19 [10.1093/bib/bbac274].

Evaluation of different computational methods for DNA methylation-based biological age

Di Lena, Pietro
;
Sala, Claudia;Nardini, Christine
2022

Abstract

In recent years there has been a widespread interest in researching biomarkers of aging that could predict physiological vulnerability better than chronological age. Aging, in fact, is one of the most relevant risk factors for a wide range of maladies, and molecular surrogates of this phenotype could enable better patients stratification. Among the most promising of such biomarkers is DNA methylation-based biological age. Given the potential and variety of computational implementations (epigenetic clocks), we here present a systematic review of such clocks. Furthermore, we provide a large-scale performance comparison across different tissues and diseases in terms of age prediction accuracy and age acceleration, a measure of deviance from physiology. Our analysis offers both a state-of-the-art overview of the computational techniques developed so far and a heterogeneous picture of performances, which can be helpful in orienting future research.
2022
Di Lena, P., Sala, C., Nardini, C. (2022). Evaluation of different computational methods for DNA methylation-based biological age. BRIEFINGS IN BIOINFORMATICS, 23(4), 1-19 [10.1093/bib/bbac274].
Di Lena, Pietro; Sala, Claudia; Nardini, Christine
File in questo prodotto:
File Dimensione Formato  
document_no_template.pdf

accesso aperto

Tipo: Postprint
Licenza: Licenza per accesso libero gratuito
Dimensione 3.83 MB
Formato Adobe PDF
3.83 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/890186
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 4
social impact