ArticleInternational journal of bioinformatics research and applications2013
Identifying radiation exposure biomarkers from mouse blood transcriptome.
Article in International journal of bioinformatics research and applications, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Candidate gene biodosimetry markers of exposure to external ionizing radiation in human blood: A systematic review.PloS one · 2018Pooled it
- Circulating cell-free methylated DNA reveals tissue-specific, cellular damage from radiation treatment.JCI insight · 2023Article
- Transcriptomes of Wet Skin Biopsies Predict Outcomes after Ionizing Radiation Exposure with Potential Dosimetric Applications in a Mouse Model.Current issues in molecular biology · 2022Article
- Nonhuman Primates with Acute Radiation Syndrome: Results from a Global Serum Metabolomics Study after 7.2 Gy Total-Body Irradiation.Radiation research · 2018Article
- Biological basis of radiation protection needs rejuvenation.International journal of radiation biology · 2017Review
- Radiation-induced alternative transcription and splicing events and their applicability to practical biodosimetry.Scientific reports · 2016Article
- Toward the development of transcriptional biodosimetry for the identification of irradiated individuals and assessment of absorbed radiation dose.Radiation and environmental biophysics · 2015Article
- Metabolic phenotyping reveals a lipid mediator response to ionizing radiation.Journal of proteome research · 2014Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Ionising radiation is a pleiotropic stress agent that may induce a variety of adverse effects. Molecular biomarker approaches possess promise to assess radiation exposure, however, the pleiotropic nature of ionising radiation induced transcriptional responses and the historically poor inter-laboratory performance of omics-derived biomarkers serve as barriers to identification of unequivocal biomarker sets. Here, we present a whole-genome survey of the murine transcriptomic response to physiologically relevant radiation doses, 2 Gy and 8 Gy. We used this dataset with the Random Forest algorithm to correctly classify independently generated data and to identify putative metabolite biomarkers for radiation exposure.
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Registered trials
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