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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 AlanniLung 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 biologyVolume
10Issue
5Pagination
168 - 178Publisher
Institution of Engineering and Technology (IET)Location
Hertfordshire, Eng.Publisher DOI
ISSN
1751-8849eISSN
1751-8857Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2016, Institution of Engineering and TechnologyUsage metrics
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No categories selectedKeywords
Science & TechnologyLife Sciences & BiomedicineCell BiologyMathematical & Computational Biologylungcancermedical diagnostic computingpatient diagnosisgenetic algorithmsfeature selectionlearning (artificial intelligence)support vector machinesmultilayer perceptronsradial basis function networksreliabilitysensitivity analysislung cancer predictioncancer-related deathcancer diagnosisgene profilesgene expression programming-based modelgene selectionGEP-based prediction modelsprediction performance evaluationsrepresentative machine learning methodssupport vector machinemultilayer perceptronradial basis function neural networkreal microarray lung cancer datasetscross-data set validationreceiver operating characteristic curveCLASSIFICATIONMACHINE
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