ArticleJournal of occupational and environmental medicine2019
Machine Learning Approach for Predicting Past Environmental Exposures From Molecular Profiling of Post-Exposure Human Serum Samples.
Article in Journal of occupational and environmental medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- PM2.5 toxin benzo[a]pyrene induces life-limiting inflammation and oxidative stress in the airway by up-regulation of TRPC6 and inactivation of β2AR/CFTR signaling.bioRxiv : the preprint server for biology · 2026Article
- Value of Bioinformatics Models for Predicting Translational Control of Angiogenesis.Circulation research · 2025Review
- Air Pollution and microRNAs: The Role of Association in Airway Inflammation.Life (Basel, Switzerland) · 2023Review
- The Impact of Air Pollution Exposure on the MicroRNA Machinery and Lung Cancer Development.Journal of personalized medicine · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo develop an approach for a retrospective analysis of post-exposure serum samples using diverse molecular profiles.
methodsThe 236 molecular profiles from 800 de-identified human serum samples from the Department of Defense Serum Repository were classified as smokers or non-smokers based on direct measurement of serum cotinine levels. A machine-learning pipeline was used to classify smokers and non-smokers from their molecular profiles.
resultsThe refined supervised support vector machines with recursive feature elimination predicted smokers and non-smokers with 78% accuracy on the independent held-out set. Several of the identified classifiers of smoking status have previously been reported and four additional miRNAs were validated with experimental tobacco smoke exposure in mice, supporting the computational approach.
conclusionsWe developed and validated a pipeline that shows retrospective analysis of post-exposure serum samples can identify environmental exposures.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.