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. 2016 Oct;10(5):168-178.
doi: 10.1049/iet-syb.2015.0082.

Lung cancer prediction from microarray data by gene expression programming

Hasseeb Azzawi  1 , Jingyu Hou  2 , Yong Xiang  2 , Russul Alanni  2
Affiliations

Lung cancer prediction from microarray data by gene expression programming

Hasseeb Azzawi et al. IET Syst Biol. 2016 Oct.

Abstract

Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models. Prediction performance evaluations and comparisons between the authors' GEP models and three representative machine learning methods, support vector machine, multi-layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross-data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.

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Figures

Fig. 1
Fig. 1
Flowchart of building a GEP classifier
Fig. 2
Fig. 2
Example of GEP ETs. Four sub‐ETs
Fig. 3
Fig. 3
Average and standard deviation of GEP models for all datasets in term of accuracy, sensitivity and specificity a Average results b Standard deviation results
Fig. 4
Fig. 4
Average of AUC ROC with standard deviation of GEP models for all datasets a Average results b Standard deviation results
Fig. 5
Fig. 5
Average of accuracy with standard deviation of GEP classifiers for all datasets a Average results b Standard deviation results
Fig. 6
Fig. 6
Average of sensitivity with standard deviation of GEP classifiers for all datasets a Average results b Standard deviation results
Fig. 7
Fig. 7
Average of specificity with standard deviation of GEP classifiers for all datasets a Average results b Standard deviation results
Fig. 8
Fig. 8
Average of AUC with standard deviation of GEP classifiers for all datasets a Average results b Standard deviation results
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