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Jennifer A. Smith

Researcher at University of Michigan

Publications -  977
Citations -  94283

Jennifer A. Smith is an academic researcher from University of Michigan. The author has contributed to research in topics: Large Hadron Collider & Standard Model. The author has an hindex of 131, co-authored 862 publications receiving 83025 citations. Previous affiliations of Jennifer A. Smith include National Institutes of Health & Imperial College London.

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Bayesian Shrinkage Estimation of High Dimensional Causal Mediation Effects in Omics Studies

Yanyi Song, +9 more
- 14 Nov 2018 - 
TL;DR: This work develops a Bayesian inference method using continuous shrinkage priors to extend previous causal mediation analysis techniques to a high-dimensional setting and identified DNA methylation regions that may actively mediate the effect of socioeconomic status (SES) on cardiometabolic outcome.
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Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention

Zhe Wang, +216 more
- 01 Sep 2022 - 
TL;DR: In this paper , a meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work.
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The Role of Chlamydia Trachomatis in High-Risk Human Papillomavirus Persistence Among Female Sex Workers in Nairobi, Kenya

Nadja A. Vielot, +5 more
- 01 Jun 2015 - 
TL;DR: Recent or concurrent CT infection was associated with prolonged hrHPV infection among a cohort of Nairobi FSWs, and management of CT could reduce risk for hrHPVs persistence.
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Measurement of the semileptonic t t ¯ + γ production cross section in pp collisions at √s=8 TeV

Albert M. Sirunyan, +2301 more
- 02 Oct 2017 - 
TL;DR: In this paper, a measurement of the cross section for top quark-antiquark (t (t) over bar) pairs produced in association with a photon in proton-proton collisions at root s = 8TeV is presented.
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Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies.

Lulu Shang, +2 more
- 20 Apr 2020 - 
TL;DR: CoNet is developed to facilitate the identification of trait-relevant tissues or cell types and can help identify specific glial cell types relevant for neurological disorders and identify disease-targeted colon tissues as relevant for autoimmune diseases.