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Computational and Mathematical Methods in Medicine - Volume 2016 2016, Article ID 6794916, 9 pages -

Research Article

Vocational School of Social Sciences, Hacettepe University, Ankara, Turkey

Department of Biostatistics, Faculty of Medicine, Hacettepe University, Ankara, Turkey

Received 29 July 2016; Revised 18 November 2016; Accepted 27 November 2016

Academic Editor: Chung-Min Liao

Copyright © 2016 Selen Yılmaz Isıkhan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background-Aim. Evaluating the success of dose prediction based on genetic or clinical data has substantially advanced recently. The aim of this study is to predict various clinical dose values from DNA gene expression datasets using data mining techniques. Materials and Methods. Eleven real gene expression datasets containing dose values were included. First, important genes for dose prediction were selected using iterative sure independence screening. Then, the performances of regression trees RTs, support vector regression SVR, RT bagging, SVR bagging, and RT boosting were examined. Results. The results demonstrated that a regression-based feature selection method substantially reduced the number of irrelevant genes from raw datasets. Overall, the best prediction performance in nine of 11 datasets was achieved using SVR; the second most accurate performance was provided using a gradient-boosting machine GBM. Conclusion. Analysis of various dose values based on microarray gene expression data identified common genes found in our study and the referenced studies. According to our findings, SVR and GBM can be good predictors of dose-gene datasets. Another result of the study was to identify the sample size of as a cutoff point for RT bagging to outperform a single RT.

Autor: Selen Yılmaz Isıkhan, Erdem Karabulut, and Celal Reha Alpar



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