Lung cancer prediction from microarray data by gene expression programming

作者：Azzawi Hasseeb^*; Hou Jingyu; Xiang Yong; Alanni Russul

来源：IET Systems Biology, 2016, 10(5): 168-178.

DOI：10.1049/iet-syb.2015.0082

• lung
• cancer
• medical diagnostic computing
• patient diagnosis
• genetic algorithms
• feature selection
• learning (artificial intelligence)
• support vector machines
• multilayer perceptrons
• radial basis function networks
• reliability
• 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

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.

• 出版日期2016-10