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Paper details
Number 1 - March 2019
Volume 29 - 2019
Machine learning techniques combined with dose profiles indicate radiation response biomarkers
Anna Papiez, Christophe Badie, Joanna Polanska
Abstract
The focus of this research is to combine statistical and machine learning tools in application to a high-throughput biological
data set on ionizing radiation response. The analyzed data consist of two gene expression sets obtained in studies of
radiosensitive and radioresistant breast cancer patients undergoing radiotherapy. The data sets were similar in principle;
however, the treatment dose differed. It is shown that introducing mathematical adjustments in data preprocessing, differentiation and trend testing, and classification, coupled with current biological knowledge, allows efficient data analysis and obtaining accurate results. The tools used to customize the analysis workflow were batch effect filtration with empirical
Bayes models, identifying gene trends through the Jonckheere–Terpstra test and linear interpolation adjustment according
to specific gene profiles for multiple random validation. The application of non-standard techniques enabled successful
sample classification at the rate of 93.5% and the identification of potential biomarkers of radiation response in breast cancer, which were confirmed with an independent Monte Carlo feature selection approach and by literature references. This
study shows that using customized analysis workflows is a necessary step towards novel discoveries in complex fields such
as personalized individual therapy.
Keywords
machine learning, gene profiling, radiation response, multiple random validation, transcription