BACKGROUND Radiotherapy has serious side effects and thus requires prudent and cautious evaluation. However, obtaining protein expression profiles is expensive and timeconsuming, making it necessary to develop a theoretical and rational procedure for predicting the radiotherapy outcome for bladder cancer when working with limited data. OBJECTIVE A procedure for estimating the performance of radiotherapy is proposed in this research. The population domain (range of the population) of proteins and the relationships among proteins are considered to increase prediction accuracy. METHODS This research uses modified extreme value theory (MEVT), which is used to estimate the population domain of proteins, and correlation coefficients and prediction intervals to overcome the lack of knowledge regarding relationships among proteins. RESULTS When the size of the training data set was 5 samples, the mean absolute percentage error rate (MAPE) was 31.6200%; MAPE fell to 13.5505% when the number of samples was increased to 30. The standard deviation (SD) of forecasting error fell from 3.0609% for 5 samples to 1.2415% for 30 samples. These results show that the proposed procedure yields accurate and stable results, and is suitable for use with small data sets. CONCLUSIONS The results show that considering the relationships among proteins is necessary when predicting the outcome of radiotherapy.

中文翻译：

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在小数据集中考虑蛋白质与膀胱癌细胞放射治疗预后的关系

背景技术放射疗法具有严重的副作用，因此需要谨慎和谨慎的评估。但是，获得蛋白质表达图谱既昂贵又费时，因此有必要开发一种理论和合理的方法来预测有限数据时膀胱癌的放射治疗结果。目的本研究提出了一种评估放疗效果的方法。蛋白质的种群结构域（种群的范围）以及蛋白质之间的关系被认为可以提高预测的准确性。方法本研究使用修正的极值理论（MEVT），该理论用于估计蛋白质的种群结构域，相关系数和预测区间，以克服对蛋白质之间关系的认识的缺乏。结果当训练数据集的大小为5个样本时，平均绝对百分比错误率（MAPE）为31.6200％；当样本数量增加到30时，MAPE降至13.5505％。预测误差的标准差（SD）从5个样本的3.0609％降至30个样本的1.2415％。这些结果表明，所提出的过程可产生准确且稳定的结果，并且适合用于小型数据集。结论结果表明，在预测放射治疗的结果时，有必要考虑蛋白质之间的关系。这些结果表明，所提出的过程可产生准确且稳定的结果，并且适合用于小型数据集。结论结果表明，在预测放射治疗的结果时，必须考虑蛋白质之间的关系。这些结果表明，所提出的过程可产生准确且稳定的结果，并且适合用于小型数据集。结论结果表明，在预测放射治疗的结果时，必须考虑蛋白质之间的关系。