Machine Learning Approximations to Predict Epigenetic Age Acceleration in Stroke Patients.
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SUBMITTER: Fernandez-Perez I
PROVIDER: S-EPMC9917369 | biostudies-literature | 2023 Feb
REPOSITORIES: biostudies-literature
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Machine Learning Approximations to Predict Epigenetic Age Acceleration in Stroke Patients.
Fernández-Pérez Isabel I Jiménez-Balado Joan J Lazcano Uxue U Giralt-Steinhauer Eva E Rey Álvarez Lucía L Cuadrado-Godia Elisa E Rodríguez-Campello Ana A Macias-Gómez Adrià A Suárez-Pérez Antoni A Revert-Barberá Anna A Estragués-Gázquez Isabel I Soriano-Tarraga Carolina C Roquer Jaume J Ois Angel A Jiménez-Conde Jordi J
International journal of molecular sciences 20230201 3
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 access ...[more]
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