Machine Learning Reveals the Contribution of Rare Genetic Variants and Enhances Risk Prediction for Coronary Artery Disease in the Japanese Population.
- DOI
- 10.1161/circgen.125.005341
- Published
- 2026 Aug
- Container
- Circulation. Genomic and precision medicine
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1161/circgen.125.005341,
title = {Machine Learning Reveals the Contribution of Rare Genetic Variants and Enhances Risk Prediction for Coronary Artery Disease in the Japanese Population.},
author = {Ieki H and Zhang S and Koyama S and Kjellberg M and Yoshida H and Kurosawa R and Matsunaga H and Miyazawa K and Enzan N and Kim C and Seo JS and Higasa K and Ozaki K and Onouchi Y and Matsuda K and Kamatani Y and Terao C and Matsuda F and Snyder MP and Komuro I and Ito K and Biobank Japan Project},
year = {2026},
journal = {Circulation. Genomic and precision medicine},
doi = {10.1161/circgen.125.005341},
url = {https://doi.org/10.1161/circgen.125.005341}
}RIS
TY - JOUR TI - Machine Learning Reveals the Contribution of Rare Genetic Variants and Enhances Risk Prediction for Coronary Artery Disease in the Japanese Population. AU - Ieki H AU - Zhang S AU - Koyama S AU - Kjellberg M AU - Yoshida H AU - Kurosawa R AU - Matsunaga H AU - Miyazawa K AU - Enzan N AU - Kim C AU - Seo JS AU - Higasa K AU - Ozaki K AU - Onouchi Y AU - Matsuda K AU - Kamatani Y AU - Terao C AU - Matsuda F AU - Snyder MP AU - Komuro I AU - Ito K AU - Biobank Japan Project PY - 2026 JO - Circulation. Genomic and precision medicine DO - 10.1161/circgen.125.005341 UR - https://doi.org/10.1161/circgen.125.005341 ER -
APA
H, I., S, Z., S, K., M, K., H, Y., R, K., H, M., K, M., N, E., C, K., JS, S., K, H., K, O., Y, O., K, M., Y, K., C, T., F, M., MP, S., I, K., K, I., & Project, B. J. (2026). Machine Learning Reveals the Contribution of Rare Genetic Variants and Enhances Risk Prediction for Coronary Artery Disease in the Japanese Population.. Circulation. Genomic and precision medicine. https://doi.org/10.1161/circgen.125.005341
Source records
- pubmed · retrieved 2026-09-26T05:58:13.604Z