Code: We have included Jupyter notebooks containing Python code for running and analyzing the gene expression, multi-omics, and non-invasive classifiers for radiation response. We have also included the gene sets and code used to compare the significant gene list from our gene expression classifier to those from the RadiationGeneSigDB database.
Contact: Contact Josh Lewis (j.e.lewis@emory.edu) with questions or feedback
Citation: If you use data, models, or code from this repository in a scientific publication, we would appreciate citation of the following paper:
Lewis, J. et al. (2020). Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance. bioRxiv 2020.08.02.233098; doi: https://doi.org/10.1101/2020.08.02.233098