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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....

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Detalles Bibliográficos
Autores principales: Zhao, Jonathan Z.L., Mucaki, Eliseos J., Rogan, Peter K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2018
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