A machine learning method for improving the accuracy of radiation biodosimetry by combining data from the dicentric chromosomes and micronucleus assays

Shuryak, I., Royba, E., Repin, M., Turner, H. C., Garty, G., Deoli, N., & Brenner, D. J. (2022). A machine learning method for improving the accuracy of radiation biodosimetry by combining data from the dicentric chromosomes and micronucleus assays. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-25453-2
Authors:
Igor Shuryak
Ekaterina Royba
Mikhail Repin
Helen C. Turner
Guy Garty
Naresh Deoli
David J. Brenner
Affiliated Authors:
Igor Shuryak
Ekaterina Royba
Mikhail Repin
Helen C. Turner
Guy Garty
Naresh Deoli
David J. Brenner
Columbia Affiliation:
Subjects:
Machine Learning (MeSH)
Genotoxicity and Carcinogenesis Mechanisms (OpenAlex Topic)
Real-Time Polymerase Chain Reaction (OpenAlex Topic)
Molecular Mechanisms of DNA Damage Response (OpenAlex Topic)
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Publication Type:
Article
Unique ID:
10.1038/s41598-022-25453-2
PMID:
36473912
DOI:
10.1038/s41598-022-25453-2
Journal:
Scientific Reports
Publication Date:
Data Source:
OpenAlex
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