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Identifying Genes to Predict Cancer Radiotherapy-Related Fatigue with Machine-Learning Methods

Wednesday, September 12, 2018 — Poster Session II

3:30 p.m. – 5:00 p.m.
FAES Terrace


  • S Tu
  • K Dickinson
  • CA Johnson
  • LN Saligan


While many factors influence the fatigue experienced by patients undergoing radiation therapy (RT), we hypothesize that expression of genes related to oxidative stress can be predictive of RT-related fatigue. In this work, we present a two-phase scheme which first selects a limited subset of genes deemed most predictive by a regularized elastic net, followed by a widely used classifier, the regularized random forest, to discriminate patients having high fatigue from low fatigue during RT. The model predicted 80% accuracy (0.80 AUC) in cross-validation. Initial results suggest that several genes are consistently selected in the proposed scheme, such as PRDX5, FHL2 and GPX4, showing promise as potential predictors for RT-related fatigue, and may provide information of its biologic underpinnings.

Category: Microbiology and Infectious Diseases