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

TL;DR

This study introduces a novel combination of continuous wavelet transform, genetic algorithms, and Bayes classifier to identify significant prognostic biomarkers in colorectal cancer, leading to the discovery of new protein markers associated with survival.

Contribution

It proposes a new feature extraction method using continuous wavelet transform and genetic algorithms for biomarker detection in colorectal cancer data.

Findings

Identified significant biomarkers including CD46

Demonstrated improved biomarker selection accuracy

Validated biomarkers with survival analysis

Abstract

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.