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Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning
Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets....
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
F1000 Research Limited
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5981198/ https://www.ncbi.nlm.nih.gov/pubmed/29904591 http://dx.doi.org/10.12688/f1000research.14048.2 |