Journal
International Journal of Molecular Science
Publication Date
2-1-2023
Volume
24
Issue
3
First Page
2759
Document Type
Open Access Publication
DOI
10.3390/ijms24032759
Rights and Permissions
Fernández-Pérez, I.; Jiménez-Balado, J.; Lazcano, U.; Giralt-Steinhauer, E.; Rey Álvarez, L.; Cuadrado-Godia, E.; Rodríguez-Campello, A.; Macias-Gómez, A.; Suárez-Pérez, A.; Revert-Barberá, A.; et al. Machine Learning Approximations to Predict Epigenetic Age Acceleration in Stroke Patients. Int. J. Mol. Sci. 2023, 24, 2759. https://doi.org/10.3390/ijms24032759 This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Recommended Citation
Fernández-Pérez, Isabel; Jiménez-Balado, Joan; Lazcano, Uxue; Giralt-Steinhauer, Eva; Rey Álvarez, Lucía; Cuadrado-Godia, Elisa; Rodríguez-Campello, Ana; Macias-Gómez, Adrià; Suárez-Pérez, Antoni; Revert-Barberá, Anna; Estragués-Gázquez, Isabel; Soriano-Tarraga, Carolina; Roquer, Jaume; Ois, Angel; and Jiménez-Conde, Jordi, "Machine learning approximations to predict epigenetic age acceleration in stroke patients." International Journal of Molecular Science. 24, 3. 2759 (2023).
https://digitalcommons.wustl.edu/oa_4/1901
Additional Links
Supplemental material is available for this article at publisher site.
