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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Liu, Yihui; Aickelin, Uwe; Feyereisl, Jan; Durrant, Lindy G (2013)
Languages: English
Types: Article
Subjects: Computer Science - Computational Engineering, Finance, and Science, Computer Science - Neural and Evolutionary Computing
Biomarkers which predict patient’s survival can play an important role in medical diagnosis and\ud treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in\ud 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\ud 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\ud 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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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  • Inferred research data

    The results below are discovered through our pilot algorithms. Let us know how we are doing!

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