Research Article Supervised Wavelet Method to Predict Patient Survival from Gene Expression Data

Copyright © 2014 Maryam Farhadian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In microarray studies, the number of samples is relatively small compared to the number of genes per sample. An important aspect of microarray studies is 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 combining wavelet approximation coefficients and Cox regression was presented. The proposed method was compared with supervised principal component and supervised partial least squares methods.The different fitted Coxmodels based on supervised wavelet approximation coefficients, the top number of supervised principal components, and partial least squares components were applied to the data.The results showed that the prediction performance of the Coxmodel based on supervised wavelet feature extraction was superior to the supervised principal components and partial least squares components. The results suggested the possibility of developing new tools based on wavelets for the dimensionally reduction of microarray data sets in the context of survival analysis. 1