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 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

What’s it worth? Exploring value uncertainty using interval questions in Contingent Valuation

In this paper we explore the idea that people only know the value they place on a given environmental change as a range, rather than as a singleton. We use the payment ladder design of contingent valuation, and take as a case study the value of coastal water quality improvements in Scotland. Kaplan-Meier survival curves, Tobit analysis and a modified Turnbull algorithm are used to explore the data. We find that most people state their values as a range, and investigate empirically the determinants of this range. The paper concludes with some thoughts concerning possible links between value ranges, context-dependence and uncertainty.contingent valuation; preference uncertainty; payment ladders; contextdependence; coastal water quality; survival analysis

Parametric Model Discrimination for Heavily Censored Survival Data

Simultaneous discrimination among various parametric lifetime models is an important step in the parametric analysis of survival data. We consider a plot of the skewness versus the coefficient of variation for the purpose of discriminating among parametric survival models. We extend the method of Cox & Oakes from complete to censored data by developing an algorithm based on a competing risks model and kernel function estimation. A by-product of this algorithm is a nonparametric survival function estimate