ArticleInternational journal of molecular sciences2023
Machine Learning Approximations to Predict Epigenetic Age Acceleration in Stroke Patients.
Article in International journal of molecular sciences, 2023. 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.
- An arithmetic method algorithm optimizing k-nearest neighbors compared to regression algorithms and evaluated on real world data sources.Scientific reports · 2026Article
- Biomarker integration and biosensor technologies enabling AI-driven insights into biological aging.Frontiers in aging · 2025Review
- Epigenetic age and long-term cancer risk following a stroke.Genome medicine · 2024Article
- Integration of epigenetic regulatory mechanisms in heart failure.Basic research in cardiology · 2023Review
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Authors and funding
15 authors.
Funding
Abstract
Age acceleration (Age-A) is a useful tool that is able to predict a broad range of health outcomes. It is necessary to determine DNA methylation levels to estimate it, and it is known that Age-A is influenced by environmental, lifestyle, and vascular risk factors (VRF). The aim of this study is to estimate the contribution of these easily measurable factors to Age-A in patients with cerebrovascular disease (CVD), using different machine learning (ML) approximations, and try to find a more accessible model able to predict Age-A. We studied a CVD cohort of 952 patients with information about VRF, lifestyle habits, and target organ damage. We estimated Age-A using Hannum's epigenetic clock, and trained six different models to predict Age-A: a conventional linear regression model, four ML models (elastic net regression (EN), K-Nearest neighbors, random forest, and support vector machine models), and one deep learning approximation (multilayer perceptron (MLP) model). The best-performing models were EN and MLP; although, the predictive capability was modest (R
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Registered trials
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