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Article
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Tier 2
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Original research — experimental, observational, or case-control study. Direct primary evidence.
Human Health Effects
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DNA Methylation Biomarkers-Based Human Age Prediction Using Machine Learning
Computational Intelligence and Neuroscience
2022
14 citations
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Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Score: 45
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0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Atef Zaguia,
Deepak Pandey,
Sandeep Painuly,
Sandeep Painuly,
Saurabh Kumar Pal,
Vivek Kumar Garg,
Neelam Goel
Summary
A machine learning model was developed to predict biological age from DNA methylation biomarkers, demonstrating performance applicable to both healthy individuals and disease cohorts. The study contributes to the growing field of epigenetic aging clocks with potential applications in assessing environmental health impacts and disease risk.
These results showed that the proposed model can predict age for healthy and diseased samples.
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