Journal
HYPERTENSION
Volume 69, Issue 2, Pages 349-+Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1161/HYPERTENSIONAHA.116.08573
Keywords
algorithm; hypertension; network; prediction; support vector machine
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Funding
- National Natural Science Foundation of China [81300253, 31430045]
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Hypertension is a major cardiovascular risk factor and accounts for a large part of cardiovascular mortality. In this work, we analyzed the properties of hypertension genes and found that when compared with genes not yet known to be involved in hypertension regulation, known hypertension genes display distinguishing features: (1) hypertension genes tend to be located at network center; (2) hypertension genes tend to interact with each other; and (3) hypertension genes tend to enrich in certain biological processes and show certain phenotypes. Based on these features, we developed a machine-learning algorithm to predict new hypertension genes. One hundred and seventy-seven candidates were predicted with a posterior probability >0.9. Evidence supporting 17 of the predictions has been found. Online Data Supplement
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