Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning.

ABSTRACT: 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. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signat

SUBMITTER: Zhao JZL 

PROVIDER: S-EPMC5981198 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature