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Biomarkers which predict patient's survival can play an important role inmedical diagnosis and treatment. How to select the significant biomarkers fromhundreds of protein markers is a key step in survival analysis. In this paper anovel method is proposed to detect the prognostic biomarkers of survival incolorectal cancer patients using wavelet analysis, genetic algorithm, and Bayesclassifier. One dimensional discrete wavelet transform (DWT) is normally usedto reduce the dimensionality of biomedical data. In this study one dimensionalcontinuous wavelet transform (CWT) was proposed to extract the features ofcolorectal cancer data. One dimensional CWT has no ability to reducedimensionality of data, but captures the missing features of DWT, and iscomplementary part of DWT. Genetic algorithm was performed on extracted waveletcoefficients to select the optimized features, using Bayes classifier to buildits fitness function. The corresponding protein markers were located based onthe position of optimized features. Kaplan-Meier curve and Cox regression modelwere used to evaluate the performance of selected biomarkers. Experiments wereconducted on colorectal cancer dataset and several significant biomarkers weredetected. A new protein biomarker CD46 was found to significantly associatewith survival time.

Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data