Considering Relationship of Proteins for Radiotherapy Prognosis of Bladder Cancer Cells in Small Data Set

Tung-I Tsai; Yaofeng Zhang; Zhigang Zhang; Gy-Yi Chao; Cheng-Chieh Tsai

doi:10.3414/ME17-02-0003

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2018; 57(04): 220-229
DOI: 10.3414/ME17-02-0003

Focus Theme – Original Articles

Georg Thieme Verlag KG Stuttgart · New York

Considering Relationship of Proteins for Radiotherapy Prognosis of Bladder Cancer Cells in Small Data Set

Authors

Tung-I Tsai

¹Collaborative Innovation Center of China Pilot Reform Exploration and Assessment-Hubei Sub-Center, Hubei University of Economics, Wuhan, China
Yaofeng Zhang

¹Collaborative Innovation Center of China Pilot Reform Exploration and Assessment-Hubei Sub-Center, Hubei University of Economics, Wuhan, China
Zhigang Zhang

²School of Information Management and Statistics, Hubei University of Economics, Wuhan, China
Gy-Yi Chao

³Department of Nursing, Chung-Hwa University of Medical Technology, Tainan, Taiwan
Cheng-Chieh Tsai

³Department of Nursing, Chung-Hwa University of Medical Technology, Tainan, Taiwan

Abstract

Summary

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.

Key words

Small data set - artificial neural network - bladder cancer - modified extreme value theory

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References

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