用机器学习方法识别基因来预测癌症放疗相关的疲劳

Wei Du, Kristin A Dickinson, Calvin A. Johnson, L. Saligan
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引用次数: 2

摘要

虽然许多因素影响放射治疗(RT)患者的疲劳经历,但我们假设与氧化应激相关的基因表达可以预测RT相关的疲劳。在这项工作中,我们提出了一个两阶段的方案,首先选择被正则化弹性网认为最具预测性的有限基因子集,然后是广泛使用的分类器,正则化随机森林,以区分在rt期间高疲劳和低疲劳的患者。该模型在交叉验证中预测准确率为80% (0.80 AUC)。初步结果表明,在该方案中,PRDX5、FHL2和GPX4等几个基因被一致选择,显示出作为rt相关疲劳的潜在预测因子的希望,并可能提供其生物学基础的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying Genes to Predict Cancer Radiotherapy-Related Fatigue with Machine-Learning Methods
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.
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