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. International Journal of Data Mining and Bioinformatics
