Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data
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 ofsu...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Published: |
2013
|
| Online Access: | https://eprints.nottingham.ac.uk/2073/ |
| _version_ | 1848790716042444800 |
|---|---|
| author | Liu, Yihui Aickelin, Uwe Feyereisl, Jan Durrant, Lindy G |
| author_facet | Liu, Yihui Aickelin, Uwe Feyereisl, Jan Durrant, Lindy G |
| author_sort | Liu, Yihui |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:17:02Z |
| format | Article |
| id | nottingham-2073 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:17:02Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-20732020-05-04T20:20:33Z https://eprints.nottingham.ac.uk/2073/ Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data Liu, Yihui Aickelin, Uwe Feyereisl, Jan Durrant, Lindy G 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. 2013 Article PeerReviewed Liu, Yihui, Aickelin, Uwe, Feyereisl, Jan and Durrant, Lindy G (2013) Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data. Knowledge-Based Systems, 37 . pp. 502-514. ISSN 0950-7051 http://www.sciencedirect.com/science/article/pii/S0950705112002687 doi:10.1016/j.knosys.2012.09.011 doi:10.1016/j.knosys.2012.09.011 |
| spellingShingle | Liu, Yihui Aickelin, Uwe Feyereisl, Jan Durrant, Lindy G Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data |
| title | Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data |
| title_full | Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data |
| title_fullStr | Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data |
| title_full_unstemmed | Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data |
| title_short | Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data |
| title_sort | wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data |
| url | https://eprints.nottingham.ac.uk/2073/ https://eprints.nottingham.ac.uk/2073/ https://eprints.nottingham.ac.uk/2073/ |