Lung cancer prediction from microarray data by gene expression programming

Azzawi, Hasseeb, Hou, Jingyu, Xiang, Yong and Alanni, Russul 2016, Lung cancer prediction from microarray data by gene expression programming, IET systems biology, vol. 10, no. 5, pp. 168-178, doi: 10.1049/iet-syb.2015.0082.

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Title Lung cancer prediction from microarray data by gene expression programming
Author(s) Azzawi, HasseebORCID iD for Azzawi, Hasseeb
Hou, JingyuORCID iD for Hou, Jingyu
Xiang, YongORCID iD for Xiang, Yong
Alanni, Russul
Journal name IET systems biology
Volume number 10
Issue number 5
Start page 168
End page 178
Total pages 11
Publisher Institution of Engineering and Technology (IET)
Place of publication Hertfordshire, Eng.
Publication date 2016-10
ISSN 1751-8849
Keyword(s) Science & Technology
Life Sciences & Biomedicine
Cell Biology
Mathematical & Computational Biology
medical diagnostic computing
patient diagnosis
genetic algorithms
feature selection
learning (artificial intelligence)
support vector machines
multilayer perceptrons
radial basis function networks
sensitivity analysis
lung cancer prediction
cancer-related death
cancer diagnosis
gene profiles
gene expression programming-based model
gene selection
GEP-based prediction models
prediction performance evaluations
representative machine learning methods
support vector machine
multilayer perceptron
radial basis function neural network
real microarray lung cancer datasets
cross-data set validation
receiver operating characteristic curve
Summary 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.
Language eng
DOI 10.1049/iet-syb.2015.0082
Field of Research 080109 Pattern Recognition and Data Mining
0601 Biochemistry And Cell Biology
0906 Electrical And Electronic Engineering
Socio Economic Objective 890301 Electronic Information Storage and Retrieval Services
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2016, Institution of Engineering and Technology
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