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.
|Title of host publication||Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021|
|Editors||Yufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li|
|Number of pages||7|
|State||Published - 2021|
|Event||2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States|
Duration: Dec 9 2021 → Dec 12 2021
|Name||Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021|
|Conference||2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021|
|Period||12/9/21 → 12/12/21|
Bibliographical noteFunding Information:
This project was partially supported by grants from the National Institutes of Health [U01TR003528, R01LM013337, R01HL107577, andR01HL55673].
© 2021 IEEE.
- genetic risk prediction
- machine learning
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Biomedical Engineering
- Health Informatics
- Information Systems and Management