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.

Introduction

Survival analysis involves the estimation of the distribution of time it takes for death to occur depending on the biology of the disease. It allows clinicians to plan a suitable treatment and counsel patients about their prognosis. In medical domains, survival analysis is mainly based on Kaplan–Meier (KM) estimator and Cox proportional hazards regression model [1], [2], which are used to evaluate the performance of prognostic markers. However how to rank these biomarkers, is a key step in survival analysis. Normally, the selection of biomarkers is based on medical knowledge and the diagnosis of the clinician [1], [2]. This may ignore potential biomarkers. Machine learning algorithms have been widely used in biomarker analysis of high dimensional medical data, such as microarray data [3], [4], [5] or mass spectrometry data [6], [7]. Despite the potential advantages over standard statistical methods, their applications to survival analysis are rare due to the difficulty in dealing with censored data [8]. Recent research has shown that machine learning methods, such as neural network [9], [10], Bayesian network [11], decision tree and Naı¨ve Bayes classifier [8], are used to improve the survival model. However, none of these methods deals with the biomarker selection in survival analysis.

In this study we propose a novel method of biomarker selection based on one dimensional continuous wavelet transform (CWT). Normally, one dimensional discrete wavelet transform (DWT) is used to reduce dimensionality in the analysis of high dimensional biomedical data [12], [13]. In biomarker detection, the feature space must have the corresponding relationship with original data space to locate the detected biomarker based on detected features. One dimensional CWT detects the feature of data at every scale and position, and keeps local property of the original data. Wavelet feature vector of CWT has the same length as the original data, and can be used to locate the biomarker in original data space.

First we perform one dimensional continuous wavelet transform at different scales on colorectal cancer data to extract the discriminant features. Then we use genetic algorithm (GA) and Bayes classifier to select the optimized features from extracted wavelet coefficients. Due to the wavelet well-known property, which reveals the local features of data (or time feature) and does not lose the position information of original data, the corresponding protein markers in the original data space are obtained based on the position of optimized wavelet features. Finally Kaplan–Meier (KM) estimator and Cox regression model were used to evaluate the performance of selected protein markers. A new protein biomarker CD46 was found to have independent prognostic significance. Recent research suggests that “the immune system might be involved in the development and progression of colorectal cancer” [1], [14]. The detection of CD46 supports their deduction or conclusions.

The rest of paper is organized as follows: In Section 2, we describe the colorectal cancer data. Our proposed method is introduced in Section 3. Wavelet feature extraction for colorectal cancer data is described in Section 4. In Section 5, genetic algorithm based on Bayes classifier is used to select the optimized features. Survival models are used to evaluate the selected biomarkers in Section 6. The experiments are conducted in Section 7, followed by discussion and concluding comments in Section 8.