title: Lung cancer prediction from microarray data by gene expression programming.
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.
authors.affiliation: School of Information Technology, Deakin University, Victoria, Australia. hazzawi@deakin.edu.au.
authors.forename: Hasseeb
authors.initials: H
authors.lastname: Azzawi
authors.affiliation: School of Information Technology, Deakin University, Victoria, Australia.
authors.forename: Jingyu
authors.initials: J
authors.lastname: Hou
authors.affiliation: School of Information Technology, Deakin University, Victoria, Australia.
authors.forename: Yong
authors.initials: Y
authors.lastname: Xiang
authors.affiliation: School of Information Technology, Deakin University, Victoria, Australia.
authors.forename: Russul
authors.initials: R
authors.lastname: Alanni
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000235:genetics; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
chemical_list: D014408:Biomarkers, Tumor