Title: Wavelet-based gene selection method for survival prediction in diffuse large B-cell lymphomas patients

Authors: Maryam Farhadian; Hossein Mahjub; Abbas Moghimbeigi; Paulo J.G. Lisboa; Jalal Poorolajal; Muharram Mansoorizadeh

Addresses: Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran ' Research Center for Health Sciences, Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran ' Modeling of Noncommunicable Disease Research Center, Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran ' School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK ' Modeling of Noncommunicable Diseases Research Center, Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran ' Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamadan, Iran

Abstract: Microarray technology allows simultaneous measurements of expression levels for thousands of genes. An important aspect of microarray studies includes the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure together with the survival prediction model. In this study, a new method based on wavelet transform for survival-relevant gene selection is presented. Cox proportional hazard model is typically used to build prediction model for patients' survival using the selected genes. The prediction model will be evaluated with the R², concordance index, likelihood ratio statistic and Akaike information criteria. The results proved that good performance of survival prediction is achieved based on the selected genes. The results suggested the possibility of developing more advanced tools based on wavelets for gene selection from microarray data sets in the context of survival analysis.

Keywords: survival analysis; gene selection; 1D wavelet transforms; microarray data; DLBCL; diffuse large B-cell lymphomas; survival prediction; lymphoma patients; gene expression data; patient survival; bioinformatics.

DOI: 10.1504/IJDMB.2015.071556

International Journal of Data Mining and Bioinformatics, 2015 Vol.13 No.2, pp.197 - 210

Received: 14 Feb 2014
Accepted: 13 Nov 2014
Published online: 31 Aug 2015 *

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