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Machine learning selected smoking-associated DNA methylation signatures that predict HIV prognosis and mortality.


ABSTRACT: BACKGROUND:The effects of tobacco smoking on epigenome-wide methylation signatures in white blood cells (WBCs) collected from persons living with HIV may have important implications for their immune-related outcomes, including frailty and mortality. The application of a machine learning approach to the analysis of CpG methylation in the epigenome enables the selection of phenotypically relevant features from high-dimensional data. Using this approach, we now report that a set of smoking-associated DNA-methylated CpGs predicts HIV prognosis and mortality in an HIV-positive veteran population. RESULTS:We first identified 137 epigenome-wide significant CpGs for smoking in WBCs from 1137 HIV-positive individuals (p?

SUBMITTER: Zhang X 

PROVIDER: S-EPMC6293604 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Machine learning selected smoking-associated DNA methylation signatures that predict HIV prognosis and mortality.

Zhang Xinyu X   Hu Ying Y   Aouizerat Bradley E BE   Peng Gang G   Marconi Vincent C VC   Corley Michael J MJ   Hulgan Todd T   Bryant Kendall J KJ   Zhao Hongyu H   Krystal John H JH   Justice Amy C AC   Xu Ke K  

Clinical epigenetics 20181213 1


<h4>Background</h4>The effects of tobacco smoking on epigenome-wide methylation signatures in white blood cells (WBCs) collected from persons living with HIV may have important implications for their immune-related outcomes, including frailty and mortality. The application of a machine learning approach to the analysis of CpG methylation in the epigenome enables the selection of phenotypically relevant features from high-dimensional data. Using this approach, we now report that a set of smoking-  ...[more]

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