ArticleComputational toxicology (Amsterdam, Netherlands)2018
Human blood gene signature as a marker for smoking exposure: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge.
Article in Computational toxicology (Amsterdam, Netherlands), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- A comparison of differential DNA methylation analysis methods for continuous outcomes: implications for epigenetic studies.Epigenomics · 2026Article
- Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth.Cell reports. Medicine · 2021Article
- Ensemble of rankers for efficient gene signature extraction in smoke exposure classification.BMC bioinformatics · 2018Article
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8 authors.
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Abstract
Crowdsourcing has emerged as a framework to address methodological challenges in omics data analysis and assess the extent to which omics data are predictive of phenotypes of interest. The sbv IMPROVER Systems Toxicology Challenge was designed to leverage crowdsourcing to determine whether human blood gene expression levels are informative of current and past smoking. Participating teams were invited to use a training gene expression dataset to derive parsimonious models (up to 40 genes) that can accurately classify subjects into exposure groups: smokers, former smokers that quit for at least one year, and never-smokers. Teams were ranked based on two classification performance metrics evaluated on a blinded test dataset. The analytical approaches of the first- and third-ranked teams, that are presented in detail in this article, involved feature selection by moderated
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