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

Liu, Yihui; Aickelin, Uwe; Feyereisl, Jan; Durrant, Lindy G

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 ofsurvival 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 2 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.

Citation

Liu, Y., Aickelin, U., Feyereisl, J., & Durrant, L. G. (2013). Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data. Knowledge-Based Systems, 37, https://doi.org/10.1016/j.knosys.2012.09.011

• Journal Article Type: Article
• Publication Date: Jan 1, 2013
• Deposit Date: Aug 9, 2013
• Publicly Available Date: Aug 9, 2013
• Journal: Knowledge-Based Systems
• Print ISSN: 0950-7051
• Electronic ISSN: 1872-7409
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 37
• DOI: https://doi.org/10.1016/j.knosys.2012.09.011
• Public URL: https://nottingham-repository.worktribe.com/output/1004814
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0950705112002687
• Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in Knowledge-Based Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Knowledge-Based Systems, 37 (2013] doi: 10.1016/j.knosys.2012.09.011; ; NB Title on accepted manuscript version differs from final, published title.