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Silvia Liverani

Researcher at Queen Mary University of London

Publications -  53
Citations -  1610

Silvia Liverani is an academic researcher from Queen Mary University of London. The author has contributed to research in topics: Population & Covariate. The author has an hindex of 17, co-authored 46 publications receiving 1154 citations. Previous affiliations of Silvia Liverani include University of Warwick & Brunel University London.

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Many analysts, one dataset: Making transparent how variations in analytical choices affect results

Raphael Silberzahn, +65 more
TL;DR: In this paper, 29 teams involving 61 analysts used the same data set to address the same research question: whether soccer referees are more likely to give red cards to dark-skin-toned players than to light-skinned-players.
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Orchestrated transcription of biological processes in the marine picoeukaryote Ostreococcus exposed to light/dark cycles

TL;DR: It is proposed that the diurnal co-regulation of genes involved in photoprotection, defence against oxidative stress and DNA repair might be an efficient mechanism, which protects cells against photo-damage thereby, contributing to the ability of O. tauri to grow under a wide range of light intensities.
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PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes

TL;DR: PReMiuM as mentioned in this paper is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model, which allows binary, categorical, count and continuous response, as well as continuous and discrete covariates.
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PReMiuM: An R Package for Profile Regression Mixture Models using Dirichlet Processes

TL;DR: PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model, an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership.
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Risk of Depression and Anxiety in Adults With Cerebral Palsy.

TL;DR: In this paper, the authors used Cox proportional hazards regression analyses to determine the risk of depression and anxiety in adults with cerebral palsy compared with an age-, sex-, and practice-matched reference group of adults without CP, using primary care data.