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Lung cancer prediction from microarray data by gene expression programming

journal contribution
posted on 2016-10-01, 00:00 authored by Hasseeb Dawood Injas Azzawi, Jingyu HouJingyu Hou, Yong XiangYong Xiang, R Alanni
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.

History

Journal

IET systems biology

Volume

10

Issue

5

Pagination

168 - 178

Publisher

Institution of Engineering and Technology (IET)

Location

Hertfordshire, Eng.

ISSN

1751-8849

eISSN

1751-8857

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2016, Institution of Engineering and Technology