  Biomarkers which predict patient's survival can play an important role in
medical diagnosis and treatment. How to select the significant biomarkers from
hundreds of protein markers is a key step in survival analysis. In this paper a
novel method is proposed to detect the prognostic biomarkers of survival in
colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes
classifier. One dimensional discrete wavelet transform (DWT) is normally used
to reduce the dimensionality of biomedical data. In this study one dimensional
continuous wavelet transform (CWT) was proposed to extract the features of
colorectal cancer data. One dimensional CWT has no ability to reduce
dimensionality of data, but captures the missing features of DWT, and is
complementary part of DWT. Genetic algorithm was performed on extracted wavelet
coefficients to select the optimized features, using Bayes classifier to build
its fitness function. The corresponding protein markers were located based on
the position of optimized features. Kaplan-Meier curve and Cox regression model
were used to evaluate the performance of selected biomarkers. Experiments were
conducted on colorectal cancer dataset and several significant biomarkers were
detected. A new protein biomarker CD46 was found to significantly associate
with survival time.
