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ClarLynda R. Williams-DeVane

Researcher at North Carolina Central University

Publications -  14
Citations -  394

ClarLynda R. Williams-DeVane is an academic researcher from North Carolina Central University. The author has contributed to research in topics: Obesity & Diabetes mellitus. The author has an hindex of 7, co-authored 14 publications receiving 298 citations. Previous affiliations of ClarLynda R. Williams-DeVane include Fisk University & Duke University.

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Maternal BMI at the start of pregnancy and offspring epigenome-wide DNA methylation: findings from the pregnancy and childhood epigenetics (PACE) consortium

Gemma C Sharp, +103 more
TL;DR: In this article, the association between pre-pregnancy maternal BMI and methylation at over 450,000 sites in newborn blood DNA, across 19 cohorts (9,340 mother-newborn pairs).
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Overall gestational weight gain mediates the relationship between maternal and child obesity

TL;DR: GWG mediated the relationship between maternal BMI and childhood adiposity and highlighted an important public health education opportunity to stress the impact of a pre-pregnancy weight and excessive GWG on the risk of child obesity for all mothers.
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Race-associated biological differences among luminal A and basal-like breast cancers in the Carolina Breast Cancer Study.

TL;DR: Compared to white women, black women had lower expression of MUC1, a suspected good prognosis gene, and higher expression of GSTT2, PSPHL, SQLE, and TYMS, suspected poor prognosis genes, after adjustment for age and PAM50 subtype.
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Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes

TL;DR: A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study and it gives the best segregation of asthmatics and non-asthmatics.
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Data-driven asthma endotypes defined from blood biomarker and gene expression data.

TL;DR: This study describes four distinct asthma endotypes identified via a purely data-driven method, specifically designed to integrate blood gene expression and clinical biomarkers in a way that provides new mechanistic insights regarding the different asthma endotype.