ArticleScientific reports2024
Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Novel RNA transcript signatures for accurate age prediction using RNA profiling.International journal of legal medicine · 2026Article
- Identification and validation of liquid biopsy-based methylation biomarkers for germ cell tumor subtypes.Communications medicine · 2026Article
- A comparison of differential DNA methylation analysis methods for continuous outcomes: implications for epigenetic studies.Epigenomics · 2026Article
- A Machine-Learning Model of Chronological Age Based on Routine Blood Biomarkers in a Central European Population: A Potential Biological Age Marker.Journal of aging research · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual's age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and preliminarily explore the biological significance of methylation-associated genes using machine learning. A biological age prediction model was constructed using human methylation data through data preprocessing, feature selection procedures, statistical analysis, and machine learning techniques. Subsequently, 15 methylation data sets were subjected to in-depth analysis using SHAP, GO enrichment, and KEGG analysis. XGBoost, LightGBM, and CatBoost identified 15 groups of methylation sites associated with biological age. The cg23995914 locus was identified as the most significant contributor to predicting biological age by calculating SHAP values. Furthermore, GO enrichment and KEGG analyses were employed to initially explore the methylated loci's biological significance.
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