Tehran University of Medical Sciences Iranian Journal of Public Health 2251-6085 43 8 2014 08 15 1091 8 Maryam Farhadian 1. Dept. of Epidemiology & Biostatistics, School of Public Health, Hamadan University of Medical Sciences , Hamadan. Iran . Hossein Mahjub 2. Research Center for Health Sciences and Dept. of Epidemiology & Biostatistics, School of Public Health, Hamadan University of Medical Sciences , Hamadan, Iran. Abbas Moghimbeigi 3. Modeling of Noncommunicable Diseases Research Center and Dept. of Epidemiology & Biostatistics, School of Public Health, Hamadan University of Medical Sciences , Hamadan, Iran. Jalal Poorolajal 3. Modeling of Noncommunicable Diseases Research Center and Dept. of Epidemiology & Biostatistics, School of Public Health, Hamadan University of Medical Sciences , Hamadan, Iran. Muharram Mansoorizadeh 4. Dept. of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University , Hamadan, Iran. 2015 10 15 An important aspect of microarray studies includes the prediction of patient survival based on their gene expression profile. To deal with the high dimensionality of this data, use of a dimension reduction procedure along with the survival prediction model is necessary. This study aimed to present a new method based on wavelet transform for survival relevant gene selection.The data included 2042 gene expression measurements from 40 patients with Diffuse Large B-Cell Lymphomas (DLBCL). The pre-processing gene expression data is decomposed using third level of the 1D discrete wavelet transform. The detail coefficients at levels 1 and 2 are filtered out and expression data reconstructed using the approximation and detailed coefficients at the third level. All the genes are then scored based on the t score. Then genes with the highest scores are selected. By using forward selection method in Cox regression model, significant genes were identified.The results showed wavelet-based gene selection method presents acceptable survival prediction. Using this method, six significant genes were selected. It was indicated the expression of GENE3359X and GENE3968X decreased the survival time, whereas the expression of GENE967X, GENE3980X, GENE3405X and GENE1813X increased the survival time.Wavelet-based gene selection method is a potentially useful tool for the gene selection from microarray data in the context of survival analysis. https://ijph.tums.ac.ir/index.php/ijph/article/view/3937 https://ijph.tums.ac.ir/index.php/ijph/article/download/3937/3598