Name
Title | ||
|---|---|---|
Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data |
Abstract | ||
|---|---|---|
Biomarkers which predict patient's survival 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 be significantly associated with survival time. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.knosys.2012.09.011 | Knowl.-Based Syst. |
Keywords | Field | DocType |
survival analysis,prognostic biomarkers,genetic algorithm,wavelet feature extraction,dimensional continuous wavelet,dimensional discrete wavelet,bayes classifier,colorectal cancer data,biomarker detection,optimized feature,selected biomarkers,significant biomarkers,survival time,cd46,feature extraction,biomarkers,colorectal cancer | Data mining,Pattern recognition,Computer science,Fitness function,Feature extraction,Continuous wavelet transform,Artificial intelligence,Discrete wavelet transform,Medical diagnosis,Genetic algorithm,Bayes classifier,Wavelet | Journal |
Volume | ISSN | Citations |
37, | Knowledge-Based Systems 37, 502-514, 2013 | 14 |
PageRank | References | Authors |
0.71 | 23 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yihui Liu | 1 | 48 | 4.49 |
| Uwe Aickelin | 2 | 1679 | 153.63 |
| Jan Feyereisl | 3 | 131 | 10.20 |
| Lindy G. Durrant | 4 | 14 | 0.71 |