@inproceedings{d2eaa9520ecd4df6aa332173ab404dd9,
title = "SNPs Filtered by Allele Frequency Improve the Prediction of Hypertension Subtypes",
abstract = "Hypertension is the leading global cause of cardiovascular disease and premature death. Distinct hypertension subtypes may vary in their prognoses and require different treatments. An individual's risk for hypertension is determined by genetic and environmental factors as well as their interactions. In this work, we studied 911 African Americans and 1,171 European Americans in the Hypertension Genetic Epidemiology Network (HyperGEN) cohort. We built hypertension subtype classification models using both environmental variables and sets of genetic features selected based on different criteria. The fitted prediction models provided insights into the genetic landscape of hypertension subtypes, which may aid personalized diagnosis and treatment of hypertension in the future.",
keywords = "genetic risk prediction, hypertension, machine learning",
author = "Yiming Li and Shah, {Sanjiv J.} and Donna Arnett and Ryan Irvin and Yuan Luo",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; Conference date: 09-12-2021 Through 12-12-2021",
year = "2021",
doi = "10.1109/BIBM52615.2021.9669758",
language = "English (US)",
series = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2796--2802",
editor = "Yufei Huang and Lukasz Kurgan and Feng Luo and Hu, {Xiaohua Tony} and Yidong Chen and Edward Dougherty and Andrzej Kloczkowski and Yaohang Li",
booktitle = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",
address = "United States",
}
