ArticleGenes2018
Human Age Prediction Based on DNA Methylation Using a Gradient Boosting Regressor.
Article in Genes, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- An ELOVL2-Based Epigenetic Clock for Forensic Age Prediction: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Predicting Post-Radiotherapy Epigenetic Age Acceleration From Pre-Treatment Data Using a Machine Learning Framework in Head and Neck Cancer Patients.Cancer medicine · 2026Article
- Ensemble learning for air quality index prediction: integrating gradient boosting, XGBoost, and stacking with SHAP-based interpretability.Scientific reports · 2026Article
- Prediction of Cerebrospinal Fluid (CSF) Pressure with Generative Adversarial Network Synthetic Plasma-CSF Biomarker Pairing.Neuroinformatics · 2025Article
- Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US.Sensors (Basel, Switzerland) · 2025Article
- Comparative analysis of regression algorithms for drug response prediction using GDSC dataset.BMC research notes · 2025Article
- Opportunities and challenges of foodborne polyphenols applied to anti-aging health foods.Food science and biotechnology · 2024Review
- Explainable AI for CHO cell culture media optimization and prediction of critical quality attribute.Applied microbiology and biotechnology · 2024Article
- Consensus Paper: Cerebellum and Ageing.Cerebellum (London, England) · 2024Review
- Predicting Chronic Hyperplastic Candidiasis Retro-Angular Mucosa Using Machine Learning.Clinics and practice · 2023Article
- DNA methylation markers for kidney function and progression of diabetic kidney disease.Nature communications · 2023Article
- Small immunological clocks identified by deep learning and gradient boosting.Frontiers in immunology · 2023Article
- Machine learning analysis on the impacts of COVID-19 on India's renewable energy transitions and air quality.Environmental science and pollution research international · 2022Article
- Prediction of East Asian Brain Age using Machine Learning Algorithms Trained With Community-based Healthy Brain MRI.Dementia and neurocognitive disorders · 2022Article
- The relationship between ageing and changes in the human blood and brain methylomes.NAR genomics and bioinformatics · 2022Article
- DNA Methylation Biomarkers-Based Human Age Prediction Using Machine Learning.Computational intelligence and neuroscience · 2022Article
- Analyzing Corin-BNP-NEP Protein Pathway Revealing Differential Mechanisms in AF-Related Ischemic Stroke and No AF-Related Ischemic Stroke.Frontiers in aging neuroscience · 2022Article
- Article
- An Epigenetic Aging Clock for Cattle Using Portable Sequencing Technology.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
All tissues of organisms will become old as time goes on. In recent years, epigenetic investigations have found that there is a close correlation between DNA methylation and aging. With the development of DNA methylation research, a quantitative statistical relationship between DNA methylation and different ages was established based on the change rule of methylation with age, it is then possible to predict the age of individuals. All the data in this work were retrieved from the Illumina HumanMethylation BeadChip platform (27K or 450K). We analyzed 16 sets of healthy samples and 9 sets of diseased samples. The healthy samples included a total of 1899 publicly available blood samples (0⁻103 years old) and the diseased samples included 2395 blood samples. Six age-related CpG sites were selected through calculating Pearson correlation coefficients between age and DNA methylation values. We built a gradient boosting regressor model for these age-related CpG sites. 70% of the data was randomly selected as training data and the other 30% as independent data in each dataset for 25 runs in total. In the training dataset, the healthy samples showed that the correlation between predicted age and DNA methylation was 0.97, and the mean absolute deviation (MAD) was 2.72 years. In the independent dataset, the MAD was 4.06 years. The proposed model was further tested using the diseased samples. The MAD was 5.44 years for the training dataset and 7.08 years for the independent dataset. Furthermore, our model worked well when it was applied to saliva samples. These results illustrated that the age prediction based on six DNA methylation markers is very effective using the gradient boosting regressor.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.