Article

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

2018-06-15

Abstract excerpt

<h4>Background: </h4> 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. The present study develops human and murine signatures with biochemically-inspired machine learning that ar...

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Literature Corpus work: ec8eec4a-d2ea-5425-8ba0-cb2668f8dba4
DOI: 10.12688/f1000research.14048.2

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