Wavelet Feature Extraction and Genetic Algorithm for Biomarker Detection in Colorectal Cancer Data

27 Pages Posted: 26 Aug 2016

See all articles by Yihui Liu

Yihui Liu

Qilu University of Technology

Uwe Aickelin

University of Nottingham, Ningbo

Jan Feyereisl

University of Nottingham - School of Computer Science

Lindy Durrant

University of Nottingham - Faculty of Medicine and Health Sciences

Date Written: January 1, 2013

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 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.

Keywords: Biomarkers, wavelet feature extraction, CD46, colorectal cancer, genetic algorithm

Suggested Citation

Liu, Yihui and Aickelin, Uwe and Feyereisl, Jan and Durrant, Lindy, Wavelet Feature Extraction and Genetic Algorithm for Biomarker Detection in Colorectal Cancer Data (January 1, 2013). Available at SSRN: https://ssrn.com/abstract=2823262 or http://dx.doi.org/10.2139/ssrn.2823262

Yihui Liu (Contact Author)

Qilu University of Technology ( email )

58 Jiefang E Rd
Jinan, Shandong 250353
China

Uwe Aickelin

University of Nottingham, Ningbo ( email )

199 Taikang East Road
Ningbo, Zhejiang 315100
China

Jan Feyereisl

University of Nottingham - School of Computer Science ( email )

Jubilee Campus
Wollaton Road
Nottingham, NG8 1BB
United Kingdom

Lindy Durrant

University of Nottingham - Faculty of Medicine and Health Sciences ( email )

Nottingham, NG7 2RD
United Kingdom

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