Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications.
- DOI
- 10.1007/s12012-026-10142-7
- Published
- 2026 Jun 15
- Container
- Cardiovascular toxicology
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s12012-026-10142-7,
title = {Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications.},
author = {Gadelmawla AF and Alsubaiei AA and AlSejari NY and Alkuwaiti MA and Mahafdah B and Abdul-Hafez HA and Mohamed AE and Hageen AW and Alharran AM and Andò G and Aronow WS},
year = {2026},
journal = {Cardiovascular toxicology},
doi = {10.1007/s12012-026-10142-7},
url = {https://doi.org/10.1007/s12012-026-10142-7}
}RIS
TY - JOUR TI - Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications. AU - Gadelmawla AF AU - Alsubaiei AA AU - AlSejari NY AU - Alkuwaiti MA AU - Mahafdah B AU - Abdul-Hafez HA AU - Mohamed AE AU - Hageen AW AU - Alharran AM AU - Andò G AU - Aronow WS PY - 2026 JO - Cardiovascular toxicology DO - 10.1007/s12012-026-10142-7 UR - https://doi.org/10.1007/s12012-026-10142-7 ER -
APA
AF, G., AA, A., NY, A., MA, A., B, M., HA, A., AE, M., AW, H., AM, A., G, A., & WS, A. (2026). Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications.. Cardiovascular toxicology. https://doi.org/10.1007/s12012-026-10142-7
Source records
- pubmed · retrieved 2026-09-25T03:33:51.831Z