TY - GEN
T1 - Identifying Genes to Predict Cancer Radiotherapy-Related Fatigue with Machine-Learning Methods
AU - Du, Wei
AU - Dickinson, Kristin
AU - Johnson, Calvin A.
AU - Saligan, Leorey N.
N1 - Funding Information:
This work was supported by the Intramural Research Programs at the National Institute of Nursing Research and the Center for Information Technology
Publisher Copyright:
© 2018 Authors.
PY - 2018/8/15
Y1 - 2018/8/15
N2 - 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.
AB - 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.
KW - Elastic net
KW - Fatigue
KW - Gene identification
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U2 - 10.1145/3233547.3233636
DO - 10.1145/3233547.3233636
M3 - Conference contribution
AN - SCOPUS:85056121407
T3 - ACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
SP - 527
BT - ACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
PB - Association for Computing Machinery, Inc
T2 - 9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2018
Y2 - 29 August 2018 through 1 September 2018
ER -